The Efficiency of Arab Stock Markets, Its Interrelationships and Interactions with Developed and Developing Stock Markets

Transcription

1 The Efficiency of Arab Stock Markets, Its Interrelationships and Interactions with Developed and Developing Stock Markets PhD Thesis

2 The Efficiency of Arab Stock Markets, Its Interrelationships and Interactions with Developed and Developing Stock Markets Bashar Abu Zarour Supervisor: Professor Konstantinious Siriopoulos A thesis submitted to the fulfillment of the requirements for the PhD program Department of Business Administration, University of Patras, Greece [email protected]. Tell: II

3 This thesis is dedicated to the memory of my father, who passed away this year, for his support, wisdom and endless knowledge. I hope that I have achieved his wish. I

4 ACKNOWLEDJMENT The process of this PhD research program is a long and complicated voyage. It is also a learning adventure. I have had the good fortune of being the recipient of advice, knowledge and effort from so many. First and foremost, I am deeply grateful to my supervisor- Professor Konstantinious Siriopoulos (Department of Business Administration, University of Patras) - for the invaluable assistance, guidance, encouragement and patience that he provided me during the preparation and execution of this research. His depth of insight, understanding, involvement and dedicated supervision at every critical stage made possible the completion of this study. Also, I would like to express my appreciation to the members of the supervision committee. I would like to thank The State Scholarship Foundation (I.K.Y) in Greece for the financial support and the facility provided for the research program and allowing me the time and freedom to pursue my research interests. I would like also to express my appreciation to nine Arabian stock markets and senior executives for their cooperation in providing me with the necessary data for the research fieldwork. Kharalambos Kokkales has kept me in touch with real life during the long years I have been working on this thesis. He has patiently listened to all my troubles during our long walks on Sundays. I am most grateful for him and his family, those who made me one member of their family. Especially, I desire to express my heartfelt obligation to my family, above all my mother, for their wish and encouragement; and for their everlasting support and deep understanding. Finally, my appreciation is highly directed to the Great Greece (as I usually like to name it) and Greek people for their hospitality and generosity which let me feel that I am in my home land. Bashar AbuZarour II

5 Abstract In an efficient market, prices adjust instantaneously toward their fundamental values; as a consequence prices should always reflect all available information. Here we consider market efficiency for new emerging markets in the Middle East region. Emerging markets are typically characterized by illiquidity, thin trading, and possibly non-linearity in returns generating process. Firstly, we adjust observed daily indices for nine Arab stock markets for infrequent trading, while the logistic map has been used to determine whether non-linearity exists in returns generating process. Next we used several econometric models to test for market efficiency. The results of runs test, variance ratio, serial correlation, BDS, and regression analysis indicate that we can reject the hypothesis that lagged price information cannot predict future prices. In other words, prices do not follow random walk properties; even after correction for thin trading. We next analyze volatility structure using GARCH models. The results of GARCH (1,1) model indicate that volatility clustering still seems to characterize some markets. While in three markets (Egypt, Kuwait, and Palestine) volatility seems to be persistent. Moreover, the results of EGARCH (1,1) model show that four markets (Bahrain, Dubai, Kuwait, and Oman) exhibit signs of leverage effect and asymmetric shocks to volatility. Compared with other emerging and international markets; Arab stock markets display relatively low rate of excessive volatility as indicated by Schwert model. Furthermore, the dependence in the second moment found to be quite enough to characterize the non-linear structure in the time series. Finally, we find that seasonality and calendar effects exist in Arab markets with three forms; day-of-the-week effect, month-of-the-year effect and the Halloween indicator. We conclude that Arab stock markets under examination are not efficient in the week form sense of efficient market hypothesis. There is a large body of empirical evidence that financial markets become highly integrated. According to modern portfolio theory, gains from international portfolio diversification are related inversely to the correlation of equity returns. The results of multivariate cointegration techniques, structural vector autoregression (SVAR) and vector autoregression (VAR) models indicate that, there is no cointegrating relation between Arab and international stock markets. The results of SVAR show that the linkage between international and Arab markets is very weak. Next we investigate the dynamic relationships among Arab markets them selves, and how do other factors; such as oil prices, affect the performance of these markets especially for Gulf Cooperation Council (GCC) stock markets. To do that, Arab markets have been divided into two sub-groups: oil production countries (GCC countries) and non-oil production countries (Jordan, Egypt, and Palestine). The results indicate the existence of long-run relation between markets, however, the short-run linkages still very weak. Non-oil countries markets can offer diversification benefits for rich GCC investors. Moreover, oil prices found to have a significant effect on GCC markets and dominate the long-run equilibrium. Oil prices play a significant role in affecting GCC markets volatility. While after the raise in oil prices; especially during the last two years, linkages between oil prices and GCC markets increased. Four GCC markets have predictive power on oil prices, with two markets to be predicted by oil prices. We conclude that Arab stock markets can offer diversification potentials for regional and international investors. Oil prices have a significant effect on GCC markets. Finally, we suggest a strategic plan to improve these markets based on two main broad goals, improving market efficiency and increasing market liberalization. To achieve these goals we identify specific targets and strategies that could be realized through tactical programs and activities. III

6 Contents 1- INTRODUCTION THE MOTIVATION OF THIS STUDY MARKETS UNDER EXAMINATION INTRODUCTIONS TO CHAPTER INTRODUCTION TO CHAPTER CONTRIBUTION EFFICIENT MARKET HYPOTHESIS (EMH) DEFINITION SPECIFICATION OF THE INFORMATION SET EMH AND ASSET PRICING MODELS Single security test Expected returns or fair game The submartingale model The Random Walk model Multiple security expected return models The Sharp-Lintner-Black model (SLB model) Market model Multifactor models Consumption-based Asset-Pricing models EMH ITS ORIGINS AND EVIDENCE The origin of EMH Weak-form efficiency (returns predictability) Random Walk Hypothesis (RWH) Return predictability Semi-strong-form of efficiency (event studies) Strong-form-efficiency (private information) EVIDENCE AGAINST EMH AND ALTERNATIVE MODELS FOR MARKET BEHAVIOR Market anomalies Long-term return anomalies Overreaction and underreaction IPOs and SEOs Mergers Stock splits Self-tenders and share repurchases Exchange listings Dividend initiations and omissions Spinoffs Proxy contests Calendar effects January effect The weekend effect (Monday effect) Holidays effects Turn of the month effect The Halloween effect Other anomalies Small firm effect Value-Line enigma Standard and Poor s (S&P) Index effect The weather Volatility tests, fads, noise trading...57 IV

7 2-5-3 Models of human behavior EVIDENCE FROM EMERGING MARKETS THE CASE OF ARAB STOCK MARKETS ARAB STOCK MARKETS AND MARKET EFFICIENCY THE FOUNDATION OF ARAB STOCK MARKETS ECONOMIC REFORMS AND DEVELOPMENT OF ARAB CAPITAL MARKETS THE PERFORMANCE OF ARAB STOCK MARKETS Market size Market liquidity Financial Valuation of Arab Stock Markets Market concentration DATA DESCRIPTION TESTING THE EFFICIENT MARKET HYPOTHESIS FOR ARAB STOCK MARKETS RANDOM WALK HYPOTHESIS (RWH) Estimating the true index-correcting for infrequent trading Regression analysis Serial correlation (autocorrelation) of the return series Non-parametric runs test Variance ratio test BDS test for returns independency THE VOLATILITY OF ARAB STOCK MARKETS RETURNS Generalized autoregressive conditional heteroskedasticity (GARCH) Exponential generalized autoregressive conditional heteroskedasticity (EGARCH) Schewrt model NON-LINEARITY AND CHAOS IN STOCK RETURNS SEASONALITY AND CALENDAR EFFECTS Day-of-the-week effect January effect or month-of the-year effect The Halloween effect EMPIRICAL RESULTS Random walk properties Volatility of returns Non-linearity in stock returns Calendar effects SUMMARY FINANCIAL INTEGRATION AND DIVERSIFICATION BENEFITS AMONG ARAB AND INTERNATIONAL STOCK MARKETS INTERNATIONAL INTEGRATION OF ARAB STOCK MARKETS Unit root test Multivariate cointegration Structural VAR (SVAR) TRANSMISSION OF STOCK PRICES MOVEMENTS BETWEEN ARAB STOCK MARKETS Granger causality Vector autoregression (VAR) EMPIRICAL RESULTS V

8 5-3-1 Integration Long-run relationship (cointegration test) Short-run relationship between Arab and international stock markets Short-run relationships among Arab stock markets Granger causality test Causality and error-correction models (VEC) Dynamic relationship between GCC stock markets and oil prices Oil prices and GCC markets volatility Long-run relationship among GCC stock markets and oil prices The rise of oil prices and GCC stock markets SUMMARY VISION AND STRATEGIC PLAN FOR ARAB STOCK MARKETS ENVIRONMENT ANALYSIS Demand and supply of financial papers Market microstructure Liberalization and markets integration Privatization Legal and regulatory environment STRENGTH, WEAKNESS, OPPORTUNITIES, AND THREATS (SWOT) ANALYSIS VISION AND STRATEGIC GOALS CONCLUSIONS REFERENCES APPENDIXES VI

9 List of Tables Table 3-1: Some Economic Indicators, Egypt.72 Table 3-2: Some Economic Indicators, Jordan...73 Table 3-3: Some Economic Indicators, Palestine 74 Table 3-4: Some Economic Indicators, Saudi Arabia 76 Table 3-5: Some Economic Indicators, Kuwait..77 Table 3-6: Some Economic Indicators, Oman 79 Table 3-7: Some Economic Indicators, UAE..81 Table 3-8: Some Economic Indicators, Bahrain 82 Table 3-9: Market Structure for Arab Stock Markets..84 Table 3-10: Accessibility of Arab Stock Markets to Foreign Investments...87 Table 3-11: Market Capitalization for Arab stock Markets.91 Table 3-12: Total Number of Listed Companies, Table 3-13: Market capitalization as Percentage of GDP.92 Table 3-14: Total Value Traded to Market Capitalization (Turn Over Ratio)..94 Table 3-15: Average Daily Trading Value (million US$)..94 Table 3-16: Total Value Traded as Percentage of GDP 95 Table 3-17: Financial Valuation of Arab Stock Markets, End of Table 3-18: Descriptive Statistics for Daily Market Returns for Arab Markets 98 Table 4-1: Random Walk Model for Observed Indices 124 Table 4-2: Random Walk Model for Observed Indices Table 4-3: Random Walk Model for Corrected Indices..126 Table 4-4: Random Walk Model for Corrected Indices..127 Table 4-5: Estimated Autocorrelations for Observed Indices Returns..128 Table 4-6: Estimated Autocorrelations for Corrected Indices Returns.128 Table 4-7: Results of Runs Test for Arab Stock Markets, Observed vs. Corrected Indicesc..130 Table 4-8: Variance Ratio Estimates and Heteroscedastic Test Statistics for Arab Stock Markets 132 Table 4-9: BDS Test Results for Observed Return Indices..133 Table 4-10: BDS Test Results for Adjusted Return Indices.134 Table 4-11: Coefficient of Variation for Daily Returns for the Three Groups Table 4-12: GARCH (1,1) Model for Daily Returns..138 Table 4-13: GARCH (1,1) Model for Weekly Returns Table 4-14: EGARCH (1,1) Model for Daily Returns 140 Table 4-15: Random Walk Models with Non-Linearity for Observed Indices 142 Table 4-16: Random Walk Models with Non-Linearity for Corrected Indices Table 4-17: OLS Results for Day-of-the-Week Effect 147 VII

10 Table 4-18: Chow Test for Structural Stability..148 Table 4-19: Day-of-the Week Effect in the First Two Moments Table 4-20: OLS Results for Month-of-the-Year Effect (January Effect) Table 4-21: Chow Test for Structural Stability Table 4-22: Month-of-the-Year Effect in the First Two Moments 152 Table 4-23: The Halloween Indicator in Arab Stock Markets..153 Table 4-24: The Halloween Indicator in Arab Stock Markets with January Effect Adjustment 154 Table 5-1: Unit Root Test for Each Individual Series, both in Levels and First Differences..180 Table 5-2: Number of Cointegrating Relations for Four VARs Models 181 Table 5-3: Johansen-Juselius Cointegration Test Results Table 5-4: Granger Causality Tests for Arab Stock Markets.185 Table 5-5: Correlation Coefficient between Daily Arab Markets Returns..186 Table 5-6: Cointegrating Equations of the VEC Models for VAR-9 and VAR Table 5-7: Significant of Zero Restrictions on Coefficients of Cointegrating Equations of the VEC Models of VAR-9 and VAR Table 5-8: VEC Model for 9 Arabian Indices in the VAR-9 Model Table 5-9: VEC Model for 6 GCC Indices in the VAR Table 5-10: Weak Exogeneity Tests of the Endogenous Variables in the VEC Models of VAR-9 and VAR Table 5-11: GARCH (1,1) Model for GCC Daily Returns with Oil Returns as a Regressor in the Variance Equation Table 5-12: Johansen-Juselius Cointegration Test Results..194 Table 5-13: Cointegrating Equations of the VEC Model for VAR Table 5-14: VEC Model for 6 GCC and Oil Price Indices in the VAR Table 5-15: Weak Exogeneity Tests of the Endogenous Variables in the VEC Model of VAR Table 5-16: VAR System for GCC Stock Markets and Oil Returns for the First Sub- Period Table 5-17: VAR System for GCC Stock Markets and Oil Returns for the Second Sub- Period Table 5-18: Variance Decomposition for the Forecast Error of Daily Market Returns for GCC Markets and Oil Returns during the First Sub-Period.200 Table 5-19: Variance Decomposition for the Forecast Error of Daily Market Returns for GCC Markets and Oil Returns during the Second Sub-Period.201 Table 6-1: Strength, Weakness, Opportunities, and Threats for Arab Stock Markets VIII

11 List of Figures Figure 2-1: Information Set..17 Figure 3-1: Arab Stock Markets Performance Compared to other International Stock Markets 9/2004-9/ Figure 3-2: Market Size for Arab Stock Markets between: Figure 3-3: Relative Market Capitalization to all Markets Figure 3-4: Market Liquidity Variables for Arab Stock Markets, Figure 3-5: Market Concentration, End of Figure 4-1: Markets Volatility (Schwert Model)..136 Figure 4-2: Average Returns Among the Two Half-Year Periods.152 Figure 5-1: Cointegrating Relations VEC-9, VEC IX

12 1- Introduction 1-1 The motivation of this study Investors require compensation for the postponement of current consumption as they put their money into a stock market. A market in which prices always fully reflect available information is called efficient (Fama, 1965, 1970). In an efficient market an investor gets what he pays for and there are no profit opportunities available to professional money managers or savvy investors. The market genuinely knows best, and the prices of securities traded are equal to the values of the dividends which these securities pay, also known as fundamental values. However, one can ask whether hypothetical trading based on an explicitly specified information set would earn superior returns. We would then need to specify an information set first. Under weak-form efficiency the information set includes only the history of prices or returns themselves. Under semi-strong-form efficiency the information set includes all information known to all market participants, like the market trading volume. Finally, strong-form efficiency means that the information set includes all information known to any market participant, including private information (Campbell et al., 1997). By definition, in an efficient market the path of prices and the return per period are unpredictable. Put more formally, the efficient market hypothesis (EMH) implies that the expected value of tomorrow s price Pt + 1, given all relevant information up to and including today denoted as Ωt, should equal today s price Pt, possibly up to a deterministic growth component µ(drift). In other words, Et[Pt + 1 Ωt] = Pt + µ, where Et denotes the mathematical expectation operator given the information at time t. In testing the EMH the model commonly used is Pt = µ + Pt e t, where e t ~ i.i.d (0, σ 2 ), or returns follow a random walk with drift Pt = µ + et. For a long time these models were maintained as an appropriate statistical model of stock market behavior. The independence of increments {et} implies that the random walk is also a fair game, but in a much stronger sense than the martingale. A martingale is a fair game, one which is neither in your favor or your opponent s, or a stochastic process {Pt}, which satisfies the following condition: Et[Pt + 1 Pt,Pt - 1,...] = Pt or Et[Pt + 1- Pt Pt,Pt - 1,...] = 0. In a random walk, independence implies not only that increments are uncorrelated, but that 1

13 any nonlinear functions of the increments are also uncorrelated. Nevertheless, the financial market literature recognizes several forms of the random walk hypothesis. First, relaxing the assumption of identically distributed increments lets us allow unconditional heteroskedasticity in the residuals, which is a useful feature given the empirically observed fact of time-variation in the volatility of many financial asset return series. And even more general version of the random walk hypothesis - the one most often tested in the recent empirical literature - may be obtained by relaxing the independence assumption of the model to include processes with dependent but uncorrelated increments. Tests of random walk may thus be categorized as follows: tests of i.i.d. increments in errors (runs tests), tests of independent increments without assuming identical distributions over time (filter rules and technical analysis) and tests of uncorrelated increments or testing the null hypothesis that autocorrelation coefficients of the first differences of the level of the random walk at various lags are all zero. The Efficient Market Hypothesis (EMH) has been the cornerstone of financial research for more than thirty years. The first comprehensive study of the dependence in stock prices can be attributed to Fama (1965) as he analyzed the daily returns of the 30 stocks that made up the Dow Jones Industrial average at the time of his study. He found low levels of serial correlation in returns at short lags, and provided evidence concerning the non-gaussian nature of the empirical distribution of the daily returns. He gave two explanations for these departures: the mixture of distributions and changing parameters hypothesis. The next step in testing the EMH focused on explaining the empirical observation that stock returns are negatively correlated in the long run. For example, the presence of positive feedback traders, who buy (sell) when prices rise (fall), causes prices to overreact to fundamentals. However, at some point in time prices start to revert back to their fundamental values, hence we observe mean reversion in returns. This behavior runs counter to the random walk hypothesis. As shocks are persistent in the case of a random walk, this offers an alternative way to test the EMH (Cuthbertson, 1996). Fama and French (1988) report that price movements for market portfolios of common stocks tend to be at least partially offset over long horizons. They found negative serial correlation in market returns over observational intervals of three to five years. Nevertheless, evidence with respect to the presence of long-term dependence in 2

14 stock returns is still inconclusive (Poterba and Summers 1988; and Jegadeesh 1990). At any rate, if the mean reversion hypothesis was rejected, researchers invalidated the asset pricing models based on Brownian notion, random walk and martingale assumptions. We now know many reasons why stock prices deviate from the random walk model. For example, the variance in stock prices is typically not constant over time, since during turbulent times the market reacts to the inflow of new information, beliefs are relatively heterogeneous and volatility is high. During quiet times beliefs are more homogenous, and much of the volatility comes from liquidity trading. This has led to the application of (G)ARCH models in stock returns. Other types of deviation are calendar anomalies, like the January effect, which had already been discovered in the stock market by Wachtel (1942), among others. Another important feature related to stock markets is market integration, and the diversification benefits that a stock market could offer for portfolio investors. Capital markets across countries or regions may exhibit varying degree of integration. Theoretically, market linkages primarily stem from the low of one price that identical assets (physical or financial) should bear the same price across countries after adjusting for transaction costs. Rational (well-informed) investors would, or perhaps should, arbitrage away price disparities, leading to more integrated markets. Over the last 20 years, financial markets become highly integrated, mainly due to reductions in the cost of information, improvements in trading systems technology and the relaxation of legal restrictions on international capital flows. The changes have accelerated the interaction among financial markets and the enlargements of capital mobility. The body of empirical evidence suggests that significant capital market integration exists among major industrialized countries, thus limiting the potential benefits from international diversification (Meric and Meric 1989; Koutmos 1996; Sinquefield 1996; Freimann 1998; Siriopoulos 1996; and Alexakis and Siriopoulos 1997). Moreover, gains from international portfolio diversification are related inversely to the correlation of equity returns, according to modern portfolio theory. In line with this theory, investors have become highly active, investing in foreign equity markets as a risk diversification strategy. 3

15 Numerous studies have demonstrated the advantages of international diversification related to low correlation between various equity markets (Eun and Resick 1984; Whealy 1988; and Meric et al. 2001). This tendency for the global markets to become integrated is a result of the increasing tendency toward liberalization and deregulation in the money and capital markets, both in developed and developing countries as well as on a bilateral and multilateral basis, commencing from; for example, trade liberalization and multilateral trade initiatives. Such liberalization is important to introduce structural reforms, to promote economic efficiency, to estimate trade and investment, and to create a necessary climate for promoting sustainable economic growth with a commitment to market-based reforms. Furthermore, long-run linkages between stock markets have important regional and global implications at the macro-level, as a domestic capital market cannot be insulated adequately from external shocks, thus the scope for independent monetary policy may become limited. It is argued in Errunza et al. (1999) that the use of return correlations at the market index level to infer gains from international diversification, involving foreign-traded assets overstates the potential benefits. The gains must be measured beyond those attainable through home-made diversification by mimicking returns on foreign market indices with domestically traded securities. In this study, we address the following research tasks: Are Arab stock markets efficient in the weak-form sense of Efficient Market Hypothesis? Is the view of predictability in stock returns (if there is) related to whether we think that these time series are non-linear? How does thin trading affect the predictability of these time series? Are Arab stock markets characterized by excessive volatility of returns, relative to other emerging and international stock markets? Having answered these questions, then we ask whether this evidence suggests that markets are efficient or not. This is the substance of chapter 4. We then address the following new issues: 4

16 Are Arab stock markets integrated among themselves and with international stock markets? If yes, how do shocks generated by international stock markets especially UK, US, and Japan affect Arab stock markets? Can Arab stock markets offer, both regional and international investors unique risk and returns characteristics to diversify international and regional portfolios? What is the effect of oil prices on the performance of Gulf Cooperation Council (GCC) stock markets? And whether these markets have predictive power on oil prices or vice versa? These issues are addressed in chapter 5. The finding should suggest whether Arab stock markets can offer diversification potentials for both international and regional portfolio investors. Moreover, the results should suggest how other factors; such as oil prices, affect stock markets, especially in GCC countries where oil prices play a crucial role in their economies. 1-2 Markets under examination. In this research, nine Arabian stock markets will be examined; these are Abu Dhabi, Bahrain, Dubai, Egypt, Jordan, Kuwait, Oman, Palestine, and Saudi Arabia. Little is known about these markets, by international standards, these markets are considered as emerging markets and relatively new. Most of them started operating over the last two decades, while Egyptian stock market; in particular, have been in existence for much longer, but until recently its level of activity was not significant. Moreover, six of these markets are from rich oil GCC countries (Abu Dhabi, Bahrain, Dubai, Kuwait, Oman, and Saudi Arabia). Except Bahrain and Oman, the other four countries are members in the Organization of Petroleum Exporting Countries (OPEC). At the end of 2003, GCC countries collectively accounted for about 21 percent of the world s 68 million barrels a day of total production, they possess 43 percent of the world s 1105 billion barrels of oil proven reserve, given that one of these countries (Saudi Arabia) is the largest oil producer and reserves in the world. 5

17 There are significant differences between Arab stock markets characteristics, in terms of market indicators, such as; number of listed companies, market capitalization, and accessibility to foreign investors. But in general, there are dominant features for these markets preventing their development, prominent among the hurdles were: deficiencies in the legal framework governing these markets, the small number of listed companies, the undiversified investment instruments, market illiquidity, narrowness and the lack of market depth, highly concentrated markets, the absence of investors awareness in general, and in many cases the lack of economic stability. In the recent years, most Arabian countries witnessed considerable steps aiming to improve their local stock markets. a number of Arab stock markets have been proceeded to separate between the supervisory and executive roles. Moreover, Arab countries can be divided into two groups regarding accessibility to direct foreign investment in stock markets, the first includes countries which do not impose any restrictions on foreign investments in financial papers; these are Egypt, Palestine, and Jordan. While the second group contains countries where such restrictions exist in varying degrees; these are the member states of GCC. The focus of this study is not on the test of market efficiency as such, but also on whether Arab stock markets are integrated with international and regional stock markets, and therefore; to what degree these markets can offer diversification potentials for regional and international portfolios investors. Moreover, GCC markets are among the markets under examination here, and it is known that oil plays a significant role in these economies. The study will investigate the effect that oil prices could have on the performance of GCC stock markets; especially after the raise in oil prices during the last two years. The reminder of this study will be as follow, we start chapter 2 with the literature review of the main work related to EMH. Starting with definition for the EMH, then identifying the information set, following with a discussion of asset pricing models and its relation with EMH. This is important since empirical tests of market efficiency especially those that examine asset price returns over extended period of time- are necessarily joint test of market efficiency and particular asset-pricing model. when the joint hypothesis is rejected, as it often, it is logically possible that this is a consequence of 6

18 deficiencies in the particular asset-price model rather than in the efficient market hypothesis, the bad model problem (Fama 1991). Consequently chapter 2 proceeds in presenting the original and evidence for EMH, then it presents evidence against EMH such as volatility and anomalies, including long-term anomalies and calendar effects, and alternative models of human behavior. Finally, evidence from emerging markets is provided. Chapter 3 presents Arab stock markets under examination, started with a survey of existing literature related to these markets, then a brief economic indicators of these countries with a brief history for each market are provided. Markets characteristics and main performance indicators are presented. Finally we present data description and main statistics. Chapter 4 is projected to examine market efficiency in Arab stock markets while chapter 5 presents the diversification potentials that Arab stock markets could offer for international investors, and the effect of oil prices on GCC stock markets, while an introduction for each of chapter 4 and 5 is coming later. The study continues with chapter 6, discussing the implication of the obtained empirical results. According to this discussion and analysis of the surrounding environment, based on two main broad strategic goals, a strategic plan has been built to improve the performance of Arab stock markets. Chapter 7 concludes the thesis. 1-3 Introductions to chapter 4 Are Arab stock markets efficient? To answer this question we start chapter 4 by testing market efficiency using most recent econometric techniques. However, the conventional tests of market efficiency have been developed for testing markets which are characterized by high level of liquidity, sophisticated investors with access to high quality and reliable information and few institutional impediments. On the other hand, emerging markets are typically characterized by illiquidity, thin trading, and possibly less well informed investors with access to unreliable information and considerable volatility. Moreover, efficiency implicitly assumes investors are rational; such rationality leads prices responding linearly to new information. However, emerging markets; especially during the early years of trading, may be characterized by investors who may not always 7

19 display risk aversion. For example, loss adverse investors; who have incurred losses, may display risk loving behavior in an attempt to recover such losses. Such examples of investors behavior may result in prices responding to information in a non-linear fashion. So, it is important to take into account the institutional features of these markets when testing for market efficiency. As a result, firstly we adjust the observed indices for infrequent trading, using Miller, Muthuswamy and Whaley (1994) approach. The procedures used to test for EMH and random walk properties, were chosen on the basis of the implications of EMH. If all relevant and available information is fully reflected in stock prices, then (a) successive price changes will be independent, so that there will be no serial correlation over time between returns. (b) Successive price changes will be identically distributed log (P t ) = log (P t-1 ) + ε t (1-1) where ε t is an independent standard variable, that is; a series of identically distributed random variables with zero mean and variance equal to unity. To test for the independence of successive price changes (condition a), we employ runs test and serial autocorrelation. Further, to test whether successive price changes are identically distributed (condition b), we use regression analysis, variance ratio, and BDS tests. All these tests were implemented for observed indices and for indices after have been corrected for infrequent trading. We proceed by testing returns volatility relative to other developed and emerging markets. For this purpose, three emerging markets (India, Turkey and Israel) and three developed markets (US, UK, and Japan) have been used to compare relative volatility with Arab stock markets. It is well reported empirical fact that the (G)ARCH property is found in examining stock returns. Schwert (1989), among others; examined how far the conditional volatility in stock returns depends on its own past volatility as well as on the volatility in other economic variables (fundamentals), such as bonds and the real out put. Later, Hamilton and Lin (1996) claimed that recessions are the primary factors that drive fluctuations in the stock returns volatility. Furthermore, asset markets are typically characterized by periods of turbulence and tranquility. That is to say, large (small) forecast errors tend to be followed by large (small) errors. Hence, the variance of the 8

20 forecast errors is often persistent, and the duration of market volatility may shed additional light on the market efficiency issue. The basic idea behind autoregressive conditional heteroskedasticity ARCH models proposed by Engle (1982) is that, the second moments of the distribution may have an autoregressive structure. Under rational expectations the forecast error is u t+1 = y t+1 -E t (y t+1 ), and the conditional distribution of y t+1 is assumed to be normal with mean µ t+1 and var(y t+1 /Ω t ) = h t+1 = a 0 +a 1 u 2 t, where Ω t is the information set available at time t. However, the ARCH process has a memory of only one period. To generalized this we can start adding lags of u t-1 in the equation h t+1, ι = 1,,q. but then the number of parameters to estimate increases rabidly (Bollerslev 1986). For example, in the GARCH (1,1) model the conditional variance depends on lagged variance terms: h t+1 = a 0 +a 1 +β 1 h t = a 0 +(a 1 + β 1 )h t +a t (u 2 t -h t ) in addition to the lagged u t where u 0 is arbitrarily assumed to be fixed and equal to zero. The parameters can be estimated by maximum likelihood techniques. Conditional on time t information Ω t, (u 2 t -h t ) has a mean of zero, and can be thought as the shock to volatility. The coefficient a 1 measures the extent to which a volatility shock today feeds through into the volatility of the next period, while a 1 + β 1 measures the rate at which this effect dies out over time. Since Engle s seminal work, many generalization of this model have been reported. For example, the GARCH (1,1) with a 1 + β 1 =1 has a unit autoregressive root, so that today s volatility affects forecasts of volatility in to the indefinite future (persistent of volatility), this is therefore known as the integrated GARCH or IGARCH model. Nelson (1991) introduced the Exponential GARCH (EGARCH) model which allows for asymmetric shocks to volatility and tests the leverage effect. The dependence of the second moment in returns captured by the (G)ARCH process is known as volatility clustering, i.e. large changes in price volatility are followed by large changes in either sign. Chapter 4 continued by using the logistic map to detect any non-linearity in returns generating process. However, the logistic map is not able to determine the precise nature of any non-linearity, but rather to ascertain whether non-linearity exists. It is appropriate that non-linearity generated by dependence in the second moment. To disentangle the non-linearity generated by changes in volatility from non-linearity arising 9

21 as a result of other causes, the standardized residuals of GARCH models will be subjected to several diagnostic tests to see what left and whether non-linearity generated by this form of dependence in the second moment or from other causes. Finally, chapter 4 concluded by using an alternative approach for testing EMH through testing for seasonality or calendar effects in stock returns. Three calendar effects have been tested the-day-of-the-week effect, Month-of-the-year effect, and the Halloween indicator. 1-4 Introduction to chapter 5 The main purpose of chapter 5 is to investigate the diversification potentials that Arab stock markets may offer for international investors, through examining whether Arab stock markets are integrated with international and regional stock markets. The analysis has been undertaken with several directions. Firstly, we start by examining the long-run relationships between Arab stock markets and international markets, which represented by the US market (S&P 500). The analysis depends on multivariate cointegration techniques proposed by Johansen (1991, 1995a), which based on the autoregressive representation discussed in Johansen (1988). However, a prerequisite for cointegration is that, non-stationary series are integrated of the same order. Therefore, the first step is to determine the order of integration for each variable. To test for unit root, the augmented Dickey-Fuller, the Phillips-Perron, and the Kwaitkowski-Phillips- Schmidt-Shin (KPSS) tests (Dickey and Fuller, 1979; Phillips and Perron, 1988; Kwaitkowski et al., 1992) have been used. The results of the multivariate cointegration indicate that Arab stock markets are not integrated with international markets in the longrun. Next, we continue to investigate the short-run relationship between Arab and international markets. More specifically, how do Arab markets react to shocks generated by international markets (US, UK, and Japan)? Using structural vector autoregression (SVAR) model and analyzing the impulse response functions. The model incorporate the assumption that the returns on each of the three international markets, affect the returns on Arab markets but NOT vice versa. A block recursive model, similar to the SVAR model used by Zha (1999), Cushman and Zha (1997), and Berument and Ince (2005) has 10

22 been used to examine the effect of a large economy s stock exchange movements (UK, US, and Japan) on a small economy s stock exchange movements (each of Arab markets). The next step in chapter 5 is to investigate the dynamic relationships between Arab stock markets both on the long- and short-runs, using multivariate cointegration and Granger causality. The total markets have been divided into two groups, oil (GCC markets) and non-oil production countries (Jordan, Egypt, and Palestine). Chapter 5 proceeds by investigating the effect of oil prices on GCC stock markets especially during the last two years, which witnessed huge rise in oil prices. Multivariate cointegration and vector autoregression models were used. Moreover, oil returns have been added as an additional regressor in the variance equation of the GARCH model, to trace the effect of oil prices on the volatility of returns. 1-5 contribution This section concludes by presenting the main empirical results of this thesis. The author asked, among other things, whether Arab stock markets are efficient in the week form sense, using daily prices for the general indices. This study concentrates on nine new Arabian emerging markets in the Middle East region. Little is known about these markets since most of them are new established, while for some of them (i.e. Palestine stock exchange) this is the first empirical work examining these markets. The first task of this research was to investigate market efficiency. For new emerging markets, the outcomes of tests of EMH are important in assessing public policy issues such as the desirability of mergers and takeovers. The EMH test results are also useful for derivative market participants, whose success precariously depends on their ability to forecast price movements, they are also important for international portfolio investors who are looking for diversification benefits in emerging markets. Moreover, they facilitate the important role of the stock market in efficient capital allocation. It is important when testing market efficiency in an emerging market, to take into account the specific features that characterize new emerging markets; such as, thin trading, nonlinearity in returns generating process, and excessive volatility. Using most recent econometric techniques, the results indicate that returns in most Arab markets are 11

23 predictable. While volatility clustering still seems to characterize some markets, volatility seems to be persistent in three markets (Egypt, Kuwait, and Palestine), other markets (Bahrain, Dubai, Kuwait, and Oman) show signs of leverage effect and asymmetric shocks to volatility. Moreover, return generating process found to be non-linear in these markets, dependent in the second moment explains enough the existing non-linearity. Furthermore, the results indicate that some kinds of anomalies exist in Arab stock markets, we conclude That the empirical evidence enables us to declare that Arab stock markets are not efficient in the weak-form sense even after correction for infrequent trading. While the dependence in the second moment found to be enough to explain the non-linearity in return generating process. These results provide new evidence to the existing literature for other emerging markets. Since many evidence of predictability in emerging markets have been found and reject the hypothesis that lagged price information cannot predict future prices (Bakaret 1995; Harvey 1995b, 1995c; Claessense et al. 1995; Buckbery 1995; Haque et al. 2001, 2004; and Bailey et al. 1990) among others. Long-term investors are often advised to invest part of their money in stocks from emerging markets, because developing markets are growing much faster than industrialized countries, and less integrated with international stock markets. However, over the last 20 years, financial markets become highly integrated; the tendency for the global markets to become more integrated is a result of the increasing tendency toward liberalization and deregulation in the money and capital markets. On the other hand and according to modern portfolio theory, gains from international portfolio diversification are related inversely to the correlation of equity markets. The integration between Arab and international markets has been investigated, using multivariate cointegration techniques, while structural vector autoregression (SVAR) has been employed to test the respond of each Arabian market to shocks originated in international markets, we conclude that 12

24 Arab stock markets can offer diversification potentials for both international and regional portfolio investors, these markets found to be segmented from international markets. In the short-term, the linkages found to be weak in general, while UK market found to have the most influence on Arab markets. Moreover, linkages among Arab markets still very weak despite the existing long-term cointegration between them, while non-oil countries markets can offer diversification benefits for rich GCC investors. These results are in line with the numerous studies that have demonstrated the advantage of international diversification related to low correlation between various equity markets, such as (Eun and Resick 1984; Wheatly 1988; Meric and Meric 1989; Baily and Stulz 1990; Divecha et al. 1992; Michaud et al. 1996; Siriopoulos 1996; Alexakis and Siriopoulos 1997; Meric et al. 2001; and Bulter and Joaquin 2002). Gulf Cooperation Council (GCC) countries are among the most important oil producing countries and a main player in the Organization of Petroleum Exporting Countries (OPEC). Producing and exporting oil play a crucial role in determining foreign earnings and governments budget revenues and expenditures for such countries, which in tern affect all aspects of daily economic life. In addition, increase in oil prices has a significant effect on local stock markets according to cash surplus. This in turn, shows the importance of studying the relation between oil prices and stock markets in GCC countries, especially after the huge increase in oil prices during the last two years. the empirical results indicate that Oil prices dominate the long-run equilibrium with GCC stock markets, and have a significant effect in determining returns volatility in these markets. Furthermore, after the raise in oil prices; the linkages between oil prices and GCC markets increased, four GCC markets have predictive power on oil prices, with only two markets to be predicted by oil prices. 13

25 These results provide new evidence to the existing literature. Few studies have looked into the relation between oil spot/future prices and stock markets, which mainly concentrates on Canada, Germany, Japan, UK, and USA (see Johnes and Kaul 1996; Huang et al. 1996; Sadorsky 1999; Papapetrou 2001; and Hammoudeh and Aleisa 2002, 2004). 14

26 2- Efficient Market Hypothesis (EMH) 2-1 Definition When the term efficient market was introduced into the economic literature thirty years ago, it was defined as a market which adjusts rapidly to new information (Fama 1969). It soon became clear, however, that which rapid adjustment to new information is an important element of an efficient market; it is not the only one. A more modern definition is that asset prices in an efficient market fully reflect all available information (Fama 1991). This implies that the market processes information rationally, in the sense that relevant information is not ignored, and systematic errors are not made. As a consequence, prices are always at levels consistent with fundamentals. The words in this definition have been chosen carefully, but they nonetheless mask some of the subtleties inherent in defining an efficient asset market. For one thing, this is a strong version of the hypothesis that could only be literally true if all available information was costless to obtain. If information was instead costly, there must be a financial incentive to obtain it. But there would not be a financial incentive if the information was already fully reflect in asset prices (Grossman and Stiglitz 1980). A weaker, but economically more realistic, version of the hypothesis is therefore that prices reflect information up to the point where the marginal benefits of acting on the information (the expected profits to be made) do not exceed the marginal costs of collecting it (Jensen 1978). Secondly, we must have a model to provide a link from economic fundamentals to asset prices. While there are candidate models in all asset markets that provide this link, no-one is confident that these models fully capture the link in any empirically convincing way. This is important since empirical tests of market efficiency-especially those that examine asset price returns over extended periods of time-are necessarily joint test of market efficiency and particular asset-pricing model. When the joint hypothesis is rejected, as it often is, it is logically possible that this is a consequence of deficiencies in the particular asset-price model rather than in the efficient market hypothesis. This is the bad model problem (Fama 1991). Finally, a comment about the word efficient It appears that the term was originally chosen partly because it provides a link with the broader economic concept of 15

27 efficiency in resource allocation. Since there are three types of efficiency: (i) pricing efficiency which refers to the notion that prices reflect rabidly in an unbiased way all available information, (ii) operational efficiency: refers to the level of costs carrying out transactions in capital markets, and (iii) allocational efficiency: refers to the extent to which capital market is allocated to the most profitable enterprises (this should be a product of pricing efficiency). When we refer to EMH, our concentration will be on pricing efficiency. Thus Fama began his 1970 review of the efficient market hypothesis (specially applied to the stock market): The primary role of the capital market is allocation of ownership of the economy s capital stock. In general terms, the ideal is a market in which prices provide accurate signals for resource allocation: that is, a market in which firms can make productioninvestment decisions, and investors can choose among the securities that represent ownership of firms activities under the assumption that securities prices at any time fully reflect all available information (Fama, 1970, p. 383) The link between an asset market that efficiently reflects available information (at least to the point consistent with the cost of collecting the information) and its role in efficient resource allocation may seem natural enough. An informationally efficient asset market need not generate allocative or production efficiency in the economy more generally. The two concepts are distinct for reasons to do with the completeness of markets and the information-revealing role of prices when information is costly and therefore valuable (Stiglitz 1981). 2-2 Specification of the information set It follows that the next intellectual stage is naturally the explanation of the content of the information set. In Fama s statement, the information which should be reflected in the price is presented as being available and relevant. How can this availability and this relevance be characterized? Robert (1967) distinguishes between three kinds, or levels, of information corresponding to three forms of informational efficiencies: 1. Weak-form efficiency: the information set includes only the history of prices or returns them selves. 16

28 2. Semi-strong form efficiency: the information set includes all information known to all participants (publicly available information). 3. Strong-form efficiency: the information set includes all information known to any market participant (private information). The categorization of the tests into weak, semi-strong, and strong form, will serve the useful purpose of allowing us to pinpoint the level of information at which the hypothesis breaks down (Fama, 1991). The information set corresponding to the weak-form of efficiency is composed by all past quoted prices of the market and only these, the weakform of the efficiency rules out the use of technical analysis, which appears as nonefficient for obtaining profits higher than those of the market it self. Here again we encounter the source of conflict with the technical analysts. The information set corresponding to the semi-strong form of efficiency consists of the preceding set of past prices, augmented by financial data of the firms. The semi-strong form of efficiency rules out classic financial analysis, to obtain profits higher than those of the market. This is the source of the conflict with the financial analysts and the economics research departments of banks. Finally, the strong form of efficiency, which includes the two preceding sets of information, is concerned with the existence of private information, i.e. not necessarily public, for example the forecasts to which the professional pension funds managers have access, unavailable to the general public. With a very strong form of efficient market, no professional managers, even provided with high skill, can obtain profits higher than those of the market on a long-time basis. This is the source of conflict with active managers of portfolios, who spend a large part of their time looking for and choosing stocks which they think will be profitable, and conjecturing as to the future development of the market. Figure 2-1: Information Set. 17

29 Fama (1991) renames these three categories. Instead of weak-form tests, he suggests tests for return predictability, since this category now covers more general area of return predictability. For the second and third categories, he proposes changes in title, not coverage. Instead of semi-strong form tests of the adjustment of prices to public announcements, he uses event studies. Instead of strong-form tests of whether specific investors have information not in market prices, he suggests the more descriptive title, tests for private information. This split of the information set into three distinct categories, leads to definition of three fields of investigation into the concept of efficiency, and then to test the efficiency. In the three cases, the gap between the return on the portfolio and that of the market must be random variable of zero expectation. The concept of efficiency thus becomes defined by the nature of the chosen information as available and relevant. 2-3 EMH and asset pricing models In most cases tests of market efficiency, are tests of a joint hypothesis (the joint hypothesis problem, Fama 1991). Thus, market efficiency per se is not testable. It must be tested with some model of equilibrium, an asset pricing model. One can say that efficiency must be tested conditional on an asset pricing model or that asset pricing models are tested conditional on efficiency. The point is that such tests are always joint evidence on efficiency and an asset-pricing model. In this section we will discuss assetpricing models which can be divided into two main categories: single security test and multiple security test Single security test That is the price or return histories of individual securities are examined for evidence of dependence that might be used as the basis of a trading system for that security. This group includes: Fair game models, Martingle model, and Random Walk Model (RWM). 18

30 Expected returns or fair game The definitional statement that in an efficient market, prices fully reflect available information is so general that it has no empirically testable implications. The process of price formation must be specified in more detail. In essence we must define somewhat more exactly what is meant by the term fully reflect. The conditions of market equilibrium can be stated in terms of expected returns. All members of the class of such expected return theories can, however, be described notationally as follows: E ( ~ p j t Φ t ) = [ + E ( ~, + 1 / 1 r j, t + 1 / Φ t )] p jt (2-1) Where E is the expected value operator; P jt is the price of security j at time t; P j,t+1 is its price at t+1; r j,t+1 is the one period percentage return (P j,t+1 -P jt )/P jt ; Φ t is a general symbol for whatever set of information is assumed to be fully reflect in the price at t; and tildes indicate that P j,t+1 and r j,t+1 are random variables at t. A fair game model as summarized in (2-1) has properties which are implications of the assumptions that (i) the conditions of market equilibrium can be stated in terms of expected returns, and (ii) the information Φ t if fully utilized by the market in forming equilibrium expected returns and thus current prices. The role of fair game models in theory of efficient markets was first recognized and studied by Mandelbrot (1966) and Samuelson (1965).The value of the equilibrium expected return E( ~ Φ ) r / j, t + 1 t projected on the basis of information Φ t would be determined from the particular expected return theory at hand. The conditional expectation notation of (2-1) is meant to imply, however, that what ever expected return model is assumed to apply, the information in Φ t is fully utilized in determining equilibrium expected returns. And this is the sense in which Φ t is fully reflected in the formation of the price P jt. The assumptions that the conditions of market equilibrium can be stated in terms of expected returns and the equilibrium expected returns are formed on the basis of the information set Φ t, have a major empirical implication. They rule out the possibility of 19

31 trading system based only on information in Φ t that has expected profits or returns. Thus, let X ( P Φ ) j, t + 1 = Pj, t + 1 E j, t + 1 / t (2-2) E ~ (2-3) Then ( x / Φ ) 0 j, t + 1 t = Which, by definition, says that the sequence {x jt } is a fair game with respect to the information sequence {Φ}. Or, equivalently, let Z ( ~ + 1 = r j, t + 1 E r j, t + 1 / Φ ), (2-4) j, t t Then ( Z / Φ ) 0 E (2-5) j, t + 1 t = So the sequence {Z it } is also a fair game with respect to the information sequence {Φ}. In economic terms, X j,t+1 is the excess market value of security j at time t+1 : it is the difference between the observed price and the expected value of the price that was projected at t on the basis of the information Φt, and similarly, Z j,t+1 is the return at t+1 in excess of the equilibrium expected return projected at t. let a ( Φ ) = [ a ( Φ ) a ( Φ ),..., a ( Φ )] t 1 t, 2 t n t be any trading system based on Φt which tells the investors the amounts aj(φt) of funds available at t that are to be invested in each of the n available securities. The total excess market value at t+1 that will be generated by such a system is V t ( Φ t )[ r j t E ( r~, + 1 j, t 1 Φ t )] n = aj / j = 1 This, from the fair game property of (2-5) has expectation: E n ~ ~ ( V t + 1 / Φ t ) = aj ( Φ t ) E ( Z j, t + / Φ ) 0. = 1 t j = 1 20

32 The submartingale model Assume that in (2-1) that for all t and Φt E ~ ( P j, t 1 / Φ t ) P Or equivalently, ( ~ r / ) 0 + jt, j, t + 1 Φ t E (2-6) this is a statement that the price sequence {P jt } for security j follows a submartingale with respect to the information sequence {Φt}, which is no thing more than that the expected value of next period s price, as projected on the basis of information Φt, is equal to or grater than the current price. If (2-6) holds as an equality (so that expected returns and price changes are zero), then the price sequence follows a martingale. A submartingale in prices has one important empirical implication. Consider the set of one security and cash mechanical trading rules, which systems that concentrate on individual securities and are that define the conditions under which the investor would hold a given security, sell it short, or simply hold cash at any time t. then the assumption of (2-6) that expected returns conditional on Φt are non-negative directly implies that such trading rules must based only on the information in Φt cannot have grater profits than a policy of always buying-and-holding the security during the future period in question The Random Walk model The statement that the current price of a security fully reflects available information was assumed to imply that successive price changes (or more usually, successive one period returns) are independent. In addition, it was usually assumed that successive changes (or returns) are identically distributed. Together the two hypotheses constitute the random walk model. Formally, the model is: ( r Φ ) f ( r ), f (2-7) j, t + 1 / t = j, t + 1 Which is the usual statement that the conditional or marginal probability distributions of an independent random variable are identical. In addition, the density function f must be the same for all t. expression (2-7) says much more than the general expected return 21

33 model summarized by (2-1). For example, if we restrict (2-1) by assuming that the expected return on security j is constant over time, then we have ( ~ r / Φ ) E ( ~ r ). E (2-8) j, t + 1 t = j, t + 1 This says that the mean of the distribution of r j,t+1 is independent of the information available at t, Φt, whereas the random walk model of (2-7) in addition says that the entire distribution is independent at Φt. Random walk model can be considered as an extension of the general expected return of fair game efficient market model, in the sense of making a more detailed statement about the economic environment. The fair game model just says that the conditions of market equilibrium can be stated in terms of expected returns, and thus it says little about the details of the stochastic process generating returns. A random walk arises within the context of such a model when the environment is such that the evolution of investor tastes and the process generating new information combine to produce equilibria in which return distributions repeat themselves through time Multiple security expected return models The multiple securities expected return models test whether securities are appropriately priced vis-à-vis one another. But to judge whether differences between average returns are appropriate, an economic theory of equilibrium expected return is required. Such as one-factor Sharp-Lintner-Black model, multifactor asset-pricing model and market model The Sharp-Lintner-Black model (SLB model) Sharp (1964) and Lintner (1965) propose the first version of this model. In this model, the expected return on security j from time t to t+1 is E ( ~ r / Φ ) j, t+ 1 t = r f, t+ 1 E + ( ~ rm, t+ 1 / Φ t ) rf σ ( ~ r / Φ ) m, t+ 1 t, t+ 1 cov σ ( ~ rj t+, ~, 1 rm, t+ 1 / Φ t ) ( r~ / Φ ) m, t+ 1 t (2-9) 22

34 Where r f,t+1 is the return from t to t+1 on an asset that is riskless in money terms; r m,t+1 is 2 the return on the market portfolio m; ( ~ r Φ ) ( r~, ~ r Φ ) cov 1 σ is the variance of the return on m; m, t+ 1 / j, t+ 1 m, t + / t is the covariance between the return on j and m; and the appearance of Φt indicates that the various expected returns, variance and covariance, could in principle depend on Φt. Though Sharp and Lintner derive (2-9) as a one-period model, the result is given a multiperiod justification and interpretation in (2-9). In words, (2-9) says that the expected one-period return on a security is the one-period riskless rate of interest r f,t+1 plus a risk premium that is proportional to ( ~ r, r~ / )/ ( r~ Φ ) cov 1 j, t + 1 m, t+ 1 Φ t σ m, t+ / t. In the Sharp-Lintner model each investor holds some combination of the riskless asset and the market portfolio, so that, given a mean-standard deviation framework, the risk of an individual asset can be measured by its contribution to the standard deviation of the return on the market portfolio. This contribution is in fact cov( ~ rj t r~ m t Φ t ) ( r~, + 1,, + 1 / / σ m, t+ 1 / Φ t ). The factor [ E( ~ r / ) ]/ ( ~ m, t+ 1 Φ t rf, t+ 1 σ rm, t+ 1 / Φt ), which is the same for all securities is then regarded as the market price of risk. The early 1970 s produce the first extensive tests of SLB model (Black, Jensen, and Scholes 1972; Blum and Friend 1973; and Fama and MacBeth 1973). These early studies suggest that the special prediction of the Sharp-Lintner version of the model, that portfolios uncorrelated with the market have expected returns equal to the risk-free rate of interest, does not fare well (the average returns on such zero-β portfolios are higher than the risk-free rate), other predictions of the model seem to do better. The most general implication of the SLB model is that equilibrium pricing implies that the market portfolio is ex ante mean-variance efficient in the sense of Markwitz (1959). Consistent with this hypothesis, the early studies suggest that (i) expected returns are a positive linear function of market β (the covariance of a security s return with the return on the market portfolio divided by the variance of market return), and (ii) β is the only measure of risk needed to explain the cross-section of expected returns. With this early support for the SLB model, there was a brief euphoric period in 1970 s when market efficiency and the SLB model seemed to be sufficient description of security returns. However, the empirical attacks on the SLB model begin in the late 1970 s with studies that identify variables that contradict the model s prediction that market β s t 23

35 suffice to describe the cross-section of expected returns. Basu (1977, 1983) shows that earning /price ratio (E/P) has marginal explanatory power; controlling for β, expected returns are positively related to E/P. Bans (1981) shows that a stock s size (price times shares) helps explain expected returns; given their market β s, expected returns on small stocks are too high, and expected returns on large stocks are too low. Bhandari (1988) shows that leverage is positively related to expected stock returns in tests that also include market β s. Finally, Chan, Hamao, and Lakonishok (1991) and Fama and French (1991) find that book-to-market equity (the ratio of the book value of a common stock to its market value) has strong explanatory power; controlling for β, higher book-to-market ratios are associated with higher expected returns Market model The market model, which is originally suggested by Markwitz (1959), hypothesizes that we can represent the return on an individual security (or portfolio) as a linear function of an index of market returns, let: r~ β ~ (2-10) j, t+ 1 = a j + jrm, t u j, t + 1 Where r j,t+1 is the rate of return on security j for time t; r m,t+1 is the corresponding return on a market index m; a j, β j are parameters that can vary from security to security; and u j,t+1 is a random disturbance. Fama, Fisher, Jensen, and Roll (1969) and the more extensive work of Blum (1968), test the market model using monthly return data. These tests indicate that (2-10) is well specified as a linear regression model in that (i) the estimated parameter â j and βˆ j remain fairly constant over long periods of time (e. g., the entire post-world war II period in the case of Blum), (ii) r m, t+1 and the estimated u ˆ j, t + 1, are close to serially independent, and (iii) the u ˆ j, t + 1 seems to be independent of r m,t+1. Thus, the observed properties of the market model are consistent with the expected return efficient market model, and in addition, the market model tells us some thing about the process generating expected returns from security to security. However, the results for the 24

36 market model are just a statistical description of the return generating process, and they are probably somewhat consistent with other models of equilibrium expected returns Multifactor models In the Sharp-Lintner-Black model, the cross-section of expected returns on securities and portfolios is described by their market β s, where β is the slope in the simple regression of a security s return on the market return. The multifactor assetpricing models of Merton (1973) and Ross (1976) generalize this result. In these models, the return-generating process can involve multiple factors, and the cross-section of expected returns is constrained by the cross-section of factor loadings (sensitivities). A security s factor loading are the lopes in a multiple regression of its return on the factors. Ross (1976) suggests the Arbitrage-Pricing Theory (APT), uses factor analysis to extract the common factors in returns and then tests whether expected returns are explained by the cross-sections of the loadings of security returns on the factors (Roll and Ross 1980; Chen 1983). Lehmann and Modest (1988) test this approach in detail. Most interesting, using models with up to 15 factors, they test whether the multifactor model explains the size anomaly of the SLB model. They find that the multifactor model leaves an unexplained size effect much like the SLB model; that is, expected returns are too high, relative to the model, for small stocks and too low for large stocks. The factor analysis approach to tests for APT leads to un-resolvable squabbles about the number of common factors in returns and expected returns (Dhrymes, Friend, and Gultekin 1984; Roll and Ross 1984; Dhrymes, Friend, Gultekin and Gultekin 1984; Trzcinka 1986; Conway and Reinganum 1988). Fama (1991) argues that the multifactor analysis approach can confirm that there is more than one common factor in returns and expected returns, which is useful. 25

37 Consumption-based Asset-Pricing models The consumption-based model of Rubinsten (1976), Lucas (1978), Breeden (1979), and others is the most elegant of the available intertemporal asset-pricing models. In Breeden s version, the interaction between optimal consumption and portfolio decisions leads to a positive linear relation between expected returns on securities and their consumption β s. (a security s consumption β s is the slope in the regression of its return on the growth rate of per capita consumption). The model thus summarizes all the incentives to hedge shifts in consumption and portfolio opportunities that can appear in Merton s (1973) multifactor model with a one-factor relation between expected returns and consumption β s. The simple elegance of the consumption model produces a sustained interest in empirical test. The tests use versions of the model that make strong assumptions about tastes (time-additive utility for consumption and constant relative risk aversion) and often about the joint distribution of consumption growth and returns (multivariate normality). Because the model is then so highly specified, it produces a rich set of testable predictions about the time series and cross-section properties of returns. The empirical work on the consumption model often jointly tests its time series and cross-section predictions, using the path breaking approach in Hansen and Singelton (1982). Estimation is with Hansen s (1982) generalized method of moments, the test is based on a χ 2 statistic that summarizes, in one number, how the data conform to the model s many restrictions. The tests usually reject. The disappointment comes when the rejection is not pursued for additional descriptive information, obscure in the χ 2 test, about which restrictions of the model (time-series, cross-section, or both) are the problem. In short, tests of the consumption model some times fail the test of usefulness; they don t enhance our ability to describe the behavior of returns, the tests of the consumption model make no attempt to deal with the anomalies that have caused problems for the SLB model. It would be interesting to confront consumption β s with variables like size and book-to-market equity, that have caused problems for the market β s of the SLB model. Given that the consumption model does not seem to fare well in tests against the SLB model or the multifactor model, however, the consumption model will do no better with the anomalies of the SLB model (Fama 1991). 26

38 Finally, it is important to emphasize that the SLB model, the consumption model, and the multifactor model are not mutually exclusive. Following Constantinides (1989), one can view the models as different ways to formalize the asset-pricing implications of common general assumption about tastes (risk aversion) and portfolio opportunities (multivariate normality). 2-4 EMH its origins and evidence The concept of efficiency is central to finance. And gained a lot of interest of popularity that the literature now is so vast and impossible to be included in a single review as correctly indicating by Fama (1991, pp.1575): The literature is now so large that a full review is impossible. Therefore, the main work about market efficiency especially that of particular interest to the purpose of this research, is included. We will start with the origins of EMH, the Random Walk Model and EMH, evidence of EMH in its three forms; weak-form tests (return predictability); semi-strong tests (event studies); and strong-form tests (private information). After that we will come to the attack on EMH through existing anomalies in the literature The origin of EMH If capital markets are sufficiently competitive, then simple microeconomics indicates that investors cannot expect to achieve superior profits from their investment strategies. But although this appears self-evident today, it was far from obvious for the majority of the century. Up to the end of the 1950s, there were few theoretical or empirical studies of securities markets; and until Cootner (1964) collected a selection of papers from a wide variety of sources, the literature was dispersed across journals in statistics, operations research, mathematics and economics. The concept of market efficiency had been anticipated at the beginning of the century in a dissertation submitted by Bachelier (1900) to the Sorbonne for his PhD in mathematics. In his opening paragraph, Bachelier recognizes that past, present and even discounted future events are reflected in market prices, but often show no apparent relation to price changes. This recognition of the informational efficiency of the market leads Bachelier to continue, in his opening paragraphs, that if the market, in effect, does 27

39 not predict its fluctuations, it does assess them as being more or less likely, and this likelihood can be evaluated mathematically. This gives rise to a brilliant analysis that anticipates not only Albert Einstein s subsequent derivation of the Einstein-Wiener process of Brown motion, but also many of the analytical results that were rediscovered by finance academics in the second half of the century. Bachelier s contribution was overlooked until it was circulated to economists by Paul Samuelson in the late 1950 s and subsequently published in English by Cootner (1964). Although there could have been an emerging theory of speculative markets during the first half of the twentieth century, this was not to be. Instead, the early literature followed the path of accumulating a variety of empirical observations that did not sit easily alongside the paradigms of economics or the beliefs of practitioners. Bachelier had concluded that commodity prices fluctuate randomly, and later studies by Working (1934) and Cowels and Jones (1937) were to show that U.S stock prices and other economic series also share the same characteristics. These studies were largely overlooked by researchers until the late of 1950 s. There was in addition, disturbing evidence about the difficulty of beating the equity markets. Alfred Cowels III, founder of the Cowels Commission and benefactor of the Economic Society, published in the launch issue in Econometrica a painstaking analysis of many thousands of stock selections made by investment professionals. Cowels (1933) finds that there was no discernable evidence of any ability to outguess the market. Subsequently, Cowels (1944) provides corroborative results for a large number of forecasts over a much longer sample period. By the 1940 s, there was therefore scattered evidence in favor of the weak and strong form efficiency of the market, though these terms were not yet in use Weak-form efficiency (returns predictability) The weak form of the efficient market hypothesis, which expanded by Fama (1991) to include returns predictability, claims that prices fully reflect the information implicit in the sequence of past prices and prices have no memory and follow random walk properties. The literature begins, therefore, with studies of weak-form market efficiency. 28

40 Random Walk Hypothesis (RWH) The random walk theory asserts that price movement will not follow any patterns or trends and that past price movements cannot be used to predict future price movements. In the early literature, discussions of the efficient markets model were phrased in terms of the even more special random walk model. Fama (1970) summarizes the early random walk literature and his own contributions and other studies of the information contained in the historical sequence of prices. It was not until the work of Samuelson (1965) and Mandelbrot (1966) that the role of fair game expected return models in the theory of efficient markets and the relationships between these models and the theory of random walks were rigorously studied. And these papers come somewhat offer the major empirical work on random walks. Until the Mandelbrot-Samuelson models appeared, there exists a large body of empirical results in search of a rigorous theory. The first statement and test of the random walk model was that of Bachelier (1900). But his fundamental principle for the behavior of prices was that speculation should be a fair game ; in particular, the expected profits to the speculator should be zero. With the benefit of stochastic processes theory, the process implied by this fundamental principle is a Martingle. After Bachelier, research on the behavior of security prices lagged until the coming of computer. In 1933 Kendall (1953) examined the behavior of weekly changes in nineteen indices of British industrial share prices and in spot prices for cotton (New York) and wheat (Chicago). After extensive analysis of serial correlations, he suggests: the series looks like a wondering one, almost as if once a week the Demon of Chance drew a random number from a symmetrical population of fixed dispersion and added it to the current price to determine the next week price (Kendall 1953, p.13). Kendall s conclusion had been suggested earlier by Working (1934) though his suggestion lacked the force provided by Kendall s empirical results, and the implications of the conclusion for stock market research and financial analysis were later underlined by Roberts (1959). But the suggestion by Kendall, Working, and Roberts that series of speculative prices may be well described by random walks was based on observations, none of these authors attempted to provide much economic rational for the hypothesis, 29

41 and, indeed, Kendall felt that the economists would generally reject it. Osborne (1959) suggests market conditions, similar to those assumed by Bachelier that would lead to a random walk. But in his model, independence of successive price changes derives from the assumption that the decisions of investors in an individual security are independent from transaction to transaction. Most of the empirical evidence in the random walk literature can easily be interpreted as tests of more general expected return or fair game models, fair game models imply the impossibility of various sorts of trading systems. Some of the random walk literature has been concerned with testing the profitability of such systems. More of the literature has, however, been concerned with tests of serial covariance of returns. The serial covariances of a fair game are zero, like a random walk, so that these tests are also relevant for the expected return models. If x t is a fair game, its unconditional expectation is zero and its serial covariance can be written in general form as: ~ ~ ~ ( X t r X t ) X t E ( X t + r X t ) f ( x t ) dx t E = /, + xt Where f indicates a density function, but if x t is a fair game, E ~ ( X t / X ) t = From this, it follows that for all lags, the serial covariances between lagged values of a fair game variable are zero. Thus, observations of a fair game variable are linearly independent. But the fair game model does not necessarily imply that the serial covariances of one-period returns are zero. In the weak form tests of this model the fair game variable is: ( r~ / r, r,...) z (2-11) j, t = r j, t E j, t j, t 1 j, t 2 But the covariance between, for example, r jt and r j,t+1 is: 30

42 E = ([ ~ r r jt [ r j, t + 1 jt E ( ~ r E ( ~ r jt j, t + 1 )][ r~ )][ E ( ~ r jt j, t + 1 E ( ~ r / r jt jt ) )]) E ( ~ r j, t + 1 )] f ( r jt ) dr jt, (2-12) And (2-11) does not imply that ( r~ / r ) E( ~ r ) E. j, t+ 1 jt = j, t + 1 In the fair game efficient markets model, the deviation of the return for t+1 from its conditional expectation is a fair game variable, but the conditional expectation itself can depend on the return observed for t. In the random walk literature, this problem is not recognized, since it is assumed that the expected return (and indeed the entire distribution of returns) is stationary through time. In practice, this implies estimating series covariances by taking cross products of deviations of observed returns from the overall sample mean return. This procedure, which represents a rather gross approximation from the view point of the general expected return efficient market model, does not seem to greatly affect the results of the covariance tests, at least for common stocks. However, there are types of nonlinear dependence that imply the existence of profitable trading systems, and yet do not imply non-zero serial covariance. The first major evidence on trading rules was Alexander s (1961, 1964). He tests a variety of trading systems, such a y% filter, which is a one security and cash trading rule, so that the results it produces are relevant for the submartingale expected return model. Alexander concludes that there is some evidence in his results against the independence assumption of the random walk model. But market efficiency does not require a random walk, and from the view point of the submartingale model, the conclusion that the filters cannot beat buy-and-hold is support for the efficient market hypothesis. Further support is provided by Fama and Blum (1966) who compare the profitability of various filters to buy-and-hold for the individual stocks of Dow-Jones Industrial Average. But some evidence in the filter tests of both Alexander and Fama-Blum that is inconsistent with the submartingale efficient markets model. In particular, the results for very small filters indicate that it is possible to devise trading schemes based on very short-term (intra-day but at most daily) price swings that will be on average outperform buy-and-hold. 31

43 These results are evidence of persistence or positive dependence in very shortterm price movement. This is consistent with the evidence for slight positive linear dependence in successive daily price changes produced by serial correlations. Thus, the filter tests, like the serial correlations, produce empirically noticeable departures from the strict implications of the efficient markets model. But, despite of any statistical significance they might have, from the economic viewpoint the departures are so small that it seems hardly justified to use them to declare the market inefficient. Another departure from the pure independence assumption of the random walk model has been noted by Osborne (1962), Fama (1965) and others. In particular, large daily price changes tend to be followed by large daily changes. The signs of the successor changes are apparently random, however, which indicates that the phenomenon represents a denial of the random walk model but not of the market efficiency hypothesis. It may be that when important new information comes into the market it cannot always be immediately evaluated precisely. Thus, sometimes the initial price will over adjust to the information, and other times it will under adjust. But since the evidence indicates that the price changes on days following the initial large change are random. In sign, the initial large change at least represents an unbiased adjustment to the ultimate price effects of the information, and this is sufficient for the expected return efficient market model. Niederhoffer and Osborne (1966) document two departures from complete randomness in common stock price changes from transaction to transaction. First, their data indicate that reversals (pairs of consecutive price changes of opposite signs) are from two to three times as likely as continuations (pairs of consecutive price changes of the same sign). Second, a continuation is slightly more frequent after a preceding continuation than after a reversal. Niederhoffer and Osborne offer explanation for these phenomena based on the market structure of the New York Stock Exchange (NYSE). But though Niederhoffer and Osborne present convincing evidence of statistically significant departures from independence in price changes from transaction to transaction, and though their analysis of their findings presents interesting insights into the process of market making on the major changes. Their analysis of market making does, however, point clearly to the existence of market inefficiency, but with respect to strong form tests of the efficient market model. 32

44 The random walk literature also has centered on the nature of the distribution of price changes, which is an important issue for the EMH. Since the nature of the distribution affects both the types of statistical tools relevant for testing the hypothesis and the interpretation of any results obtained. A model implying normally distributed price changes was first proposed by Bachelier (1900), who assumed that price changes from transaction to transaction are independent, identically distributed random variables with finite variances. If transactions are fairly uniformly spread across time, and if the number of transactions per day, week, or month is very large, then the Central Limit Theorem leads us to expect that these prices changes will have normal or Guassian distribution. Osborne (1959), Moore (1962), and Kendall (1953) although their empirical evidence support the normality hypothesis, but all observed high tails in their data distribution. Drawing on these finding and some empirical work of his own, Mandelbort (1963) then suggests that these departures from normality could be explained by a more general form of Bachelier model. In particular, if one does not assume that distributions of price changes from transaction necessarily have finite variance, then the limiting distributions for price changes over longer differencing intervals could be any member of the stable class, which includes the normal as special case. Non-normal stable distributions have higher tails than the normal, and so can account for this empirically observed feature of distributions of price changes. Fama (1965), after extensive testing, concludes that non-normal stable distributions are a better description of distributions of daily returns on common stocks than the normal. This conclusion is also supported by the empirical work of Blum (1968) on common stocks, and it has been extended to U.S Government Treasury Bills by Roll (1968) Return predictability The tests of weak-form-efficiency up to this point, focused on forecasting returns from past returns. However, and after 1970 the tests reject the market efficiency-constant expected returns model that seems to do well in the early work. Since weak-form tests concerned with the forecast power of past returns, Fama (1991) expands the coverage of the first category of EMH to cover the more general area of tests for return predictability. Such as forecast power of variables like dividend yield (D/P), earning/price ratio (E/P), 33

45 and term structure variables. Moreover, the early work of weak-form-efficiency concentrated on the predictability of daily, weekly, and monthly returns, but the recent tests also examine the predictability of returns for long horizons. In the pre-1970 literature the common equilibrium-pricing model in tests of stock market efficiency is the hypothesis that expected returns are constant through time. Market efficiency then implies that returns are unpredictable from past returns or other past variables, and the best forecast of return is its historical mean. After the 1970 s, daily data on NYSE and AMEX stocks back to 1962, makes it possible to estimate precisely the autocorrelation in daily and weekly returns. Lo and MacKinlay (1988) find that weekly returns on portfolios of NYSE stocks grouped according to size, show reliable positive autocorrelation. The autocorrelation is stronger for portfolios of small stocks. This suggests, however, that the results are due in part to the nonsynchronous trading effect (Fisher 1966). Fisher emphasizes that spurious autocorrelation in portfolio returns, induced by nonsynchronous closing trades for securities in the portfolio, is likely to be more important for portfolios titled toward small stocks. Conrad and Kaul (1988) examine the autocorrelation of Wednesday-to-Wednesday returns for size-grouped portfolios of stocks that trade on both Wednesdays. Like Lo and MacKinlay (1988), they find that weekly returns are positively autocorrelated, and more for portfolios of small stocks. French and Roll (1986) find that stock prices are more variable when the market is open. On an hourly basis, the variance of price changes is 72 times higher during trading hours than during weekend nontrading hours. Likewise, the hourly variance during trading hours is 13 times the overnight nontrading hourly variance during the trading week. One of the explanations that French and Roll test is a market inefficiency hypothesis popular among academics; specifically, the higher variance of price changes during trading hours is partly transitory, the results of noise trading by uninformed investors (Black 1986). Under this hypothesis, pricing errors due to noise trading are eventually reversed, and this induces negative autocorrelation in daily returns. With the Center for Research in Security Prices (CRSP) daily data back to 1962, post s research is able to show confidently that daily and weekly returns are predictable from past returns. The work thus rejects the old market efficiency-constant expected returns model on a statistical basis. The results tend to confirm the conclusion 34

46 of the early work that, at least for individual stocks, variation in daily and weekly expected returns is a small part of the variance of returns. The early literature does not interpret the autocorrelation in daily and weekly returns as important evidence against the joint hypothesis of market efficiency and constant expected returns. The argument is that, even when the autocorrelations deviate reliably from zero, they are close to zero and thus economically insignificant. The view that autocorrelation of short-horizon returns close to zero imply economic insignificance is challenged by Shiller (1984) and Summers (1986). They present simple models in which stock prices take large slowly decaying swings away from fundamentals values (fads, or irrational bubbles), but short-horizon returns have little autocorrelation. In the Shiller-Summers model, the market is highly inefficient, but in away that is missed in tests on short-horizon returns. Stambaugh (1986) points out that although the Shiller-Summers model can explain autocorrelation of short-horizon returns that are close to zero. The long swings away from fundamental value proposed in the model imply that long-horizon returns have strong negative autocorrelation. Since the swings away from fundamental value are temporary, over long horizons they tend to be reversed. Another implication of the negative autocorrelation induced by temporary price movements is that the variance of returns should grow less than in proportion to the return horizon. Fama and French (1988a) find that the autocorrelations of returns on diversified portfolios of NYSE stocks for the period have the pattern predicted by Shiller-Summers model. Even with 60 years of data, however, the tests on long-horizon returns imply small sample size and low power. When Fama and French delete the period from the tests, the evidence of strong negative autocorrelation in 3-to5 years returns disappears. Similarly, Poterba and Summers (1988) find that, for N from 2 to 8 years, the variance of the N-year returns on diversified portfolios grows much less than in proportion to N, this is consistent with the hypothesis that there is negative autocorrelation in returns induced by temporary price swings. Even with 115 years ( ) of data, however, the variance tests for long-horizon returns provide weak statistical evidence against the hypothesis that returns have no autocorrelation and prices are random walks. DeBondt and Thaler (1985, 1987) mount an aggressive empirical attack on market efficiency, directed at unmasking irrational bubbles. They find that the NYSE stocks 35

47 identified as the most extreme losers over a 3-to 5 years period tend to have strong returns relative to the market during the following years, especially in January of the following years. Conversely, the stocks identified as extreme winners tend to have weak returns relative to the market in subsequent years. They attribute these results to market overreaction to extreme bad or good news about firms. Jagadeesh (1990), Lehmann (1990), Lo and MacKinlay (1990) also find reversal behavior in the weekly and monthly returns of extreme winners and losers. Lehmann s weekly reversals seem to lack economic significance. When he accounts for spurious reversals due to bouncing between bid and ask price, trading costs of 0.2% per turnaround transaction suffice to make the profits from his reversal trading rules close to zero. An autocorrelation is the slope in a regression of the current return on a past return. Since variation through time in expected returns is only part of the variation in returns, tests based on autocorrelations lack power because past realized returns are noisy measures of expected returns. Power in tests for return predictability can be enhanced if one can identify forecasting variables that are less noisy proxies for expected returns than past returns. A Puzzle of the 1970 s was to explain why monthly stock returns are negatively related to expected inflation (Nelson 1976; Jaffe and Mandelker 1976; Fama 1981) and the level of short-term interest rates (Fama and Schwert 1977). Like the autocorrelation tests, however, the early work on forecasts of short-horizon returns from expected inflation and interest rates suggests that the implied variation in expected return is a small part of the variance of returns. However, for long-horizon returns, predictable variation is a larger part of return variances. Fama and French (1988b) use dividend yields D/P to forecast returns on the value-weighted and equally weighted portfolios of NYSE stocks for horizons from 1 month to 5 years. D/P explains small fractions of monthly and quarterly return variances. Fractions of variances explained grow with the return horizon, however, and are around 25% for 2-to 4 years returns. Campbell and Shiller (1988b) find that E/P ratios have reliable forecast power that also increased with return horizon. Fama and French (1988b) argue that dividend yields track highly autocorrelated variation in expected returns that becomes a large fraction of return variation for longer return horizons. The increasing fraction of the variance of long-horizon returns explained by D/P is thus due in large part 36

48 to the slow mean reversion of expected returns. Examining the forecast power of variables like D/P and E/P over a range of returns horizons nevertheless gives striking perspective on the implications of slow-moving expected returns for the variation of returns. Fama and French (1989) suggest a different way to judge the implications of return predictability for market efficiency. They argue that if variation in expected returns is common to different securities, then it is probably a rational result of variation in tastes for current versus future consumption or in the investment opportunities. They push the common expected returns argument for market efficiency one step farther, they argue that there are systematic patterns in the variation of expected returns through time that suggest that it is rational. They find that the variation in expected returns tracked by D/P and the default spread (the slopes in the regressions of returns on D/P or the default spread) increase from high-grade bonds to low-grade bonds, from bonds to stocks, and from large stocks to small stocks, this ordering corresponds to intuition about the risks of the securities. On the other hand, the variation in expected returns tracked by the term spread is similar for all long-term securities (bonds and stocks), which suggests that it reflects variation in a common premium for maturity risks. The general message of the Fama- French tests is that D/P and the default spread are high (expected returns on stocks and bonds are high) when times have been poor (growth rates of output have been persistently low). On the other hand, the term spread and expected returns are high when economic conditions are weak but anticipated to improve (future growth rates of output are high). Persistent poor times may signal low wealth and higher risks in security returns, both of which can increase expected returns. In addition, if poor times (and low incomes) are anticipated to be partly temporary, expected returns can be high because consumers attempt to smooth consumption from the future to the present Semi-strong-form of efficiency (event studies) Semi-strong form tests of efficient markets model are concerned with whether current prices fully reflect all obviously publicly available information. Fama (1991) proposes changes in title, not coverage. He uses the title event studies instead of semistrong-form tests of the adjustment of prices to public announcements. The study of stock 37

49 splits by Fama, Fisher, Jensen, and Roll (1969), was the original event study. The purpose of the study was to have a work that made extensive use of the newly developed CRSP monthly NYSE file at that time. They find that, if information in stock splits concerning the firm s future dividend payments is on average fully reflected in the price of a split share at the time of the split. Event studies are now an important part of finance, especially corporate finance. In 1970 s there was little evidence on the central issues of corporate finance. Now we are overwhelmed with results, mostly from event studies, using simple tools, this research documents interesting regularities in the response of stock prices to investment decisions; financing decisions; and changes in corporate control. Regarding EMH, the CRSP files of daily returns on NYSE, AMEX, and NASDAQ stocks are a major boost for the precision of event studies. When the announcement of an event can be dated to the day, daily data allow precise measurement of the speed of stock-price response- the central issue of market efficiency. Another powerful advantage of daily data is that they can attenuate or eliminate the jointhypothesis problem, that market efficiency must be tested jointly with an asset-pricing model. The typical result in event studies on daily data is that, on average, stock prices seem to adjust within a day to the event announcement. The fact that quick adjustment is consistent with efficiency is noted, and then the studies move on to other issues. In short, in the only empirical work where the joint hypothesis problem is relatively unimportant, the evidence typically says that, with respect to firm-specific events, the adjustment of stock prices to new information is efficient (Fama 1991). Moreover, when part of the response of prices to information seems to occur slowly, event studies become subject to the joint-hypothesis problem. For example, the early merger work finds that the stock prices of acquiring firms hardly react to merger announcements, but therefore they drift slowly down (Asquith 1983). One possibility is that acquiring firms on average pay too much for target firms, but the market only realizes this slowly; the market is inefficient (Roll 1986). Another possibility is that the post-announcement drift is due to bias in measured abnormal returns (Frank, Haris, and Titman 1991). Still another possibility is that the drift in the stock prices of acquiring firms in the early merger studies is sample-specific. Mitchell and Lehn (1990) find no 38

50 evidence of post announcement drift during the period for a sample of about 400 acquiring firms. Post-announcement drift in abnormal return is also a common result in studies of the response of stock prices to earnings announcements. Predictability, there is a raging debate on the extent to which the drift can be attributed to problems in measuring abnormal returns (Bernard and Thomas 1989; Ball, Kothari, and Watta 1990). Bernard and Thomas (1990) identify a more direct challenge to market efficiency in the way stock prices adjusted to earnings announcements; they argue that the market does not understand the autocorrelation of quarterly earnings. As a result, part of the 3-day stockprice response to this quarter s earning announcement is predictable from earning 1 to 4 quarters back. In short, some event studies suggest that stock prices do not respond quickly to specific information. Given the event study boom of the last 20 years, however, some anomalies, spurious and real, are inevitable. Moreover, event studies are the cleanest evidence we have on efficiency (the least encumbered by the joint-hypothesis problem). With few exceptions, the evidence is supportive (Fama 1991) Strong-form-efficiency (private information) The strong tests of the efficient markets model are concerned with whether all available information is fully reflected in prices in the sense that no individual has higher expected trading profits than others because he has monopolistic access to some information. Niederhoffer and Osborn (1966) show that NYSE specialists use their monopolistic access to the book of limit orders to generate trading profits. Scholes (1972) shows that corporate insiders have access to information not reflected in prices. Jaffe (1974) finds that for insiders the stock market is not efficient; insiders have information that is not reflected in prices, and market does not react quickly to public information about insider trading, outsiders can profit from the knowledge that there has been heavy insider trading for up to 8 months after information about trading becomes public. Seyhun (1986) offers an explanation, he confirms that insiders profit from their trades, but he does not confirm Jaffe s finding that outsiders can profit from public information about insider trading. Seyhun argues that Jaffe s outsider profits arise because he uses the Sharp-Lintner-Black model for expected returns, Seyhun shows that 39

51 insider buying is relatively more important in small firms, whereas insider selling is more important in large firms. There is a general message in Seyhun s results, highly constrained asset-pricing model like the Sharp-Lintner-Black model are surely false. They have systematic problems explaining the cross-section of expected returns that can look like market inefficiencies. In market-efficiency tests, one should avoid models that put strong restrictions on the cross-section of expected returns, if that is consistent with the purpose at hand. Concretely, one should use formal asset-pricing models when the phenomenon studied concerns the cross-section of expected returns (e.s., tests for size, leverage, and E/P effects). But when the phenomenon is firm-specific (most event studies), one can use firm-specific models, like the market model or historical average returns, to abstract from normal expected returns without putting unnecessary constraints on the cross-section of expected returns (Fama 1991). For security analysis, the Value Line Investment Survey publishes weekly rankings of 1700 common stocks into 5 groups, group 1 has the best return prospect and group 5 the worst. There is evidence that, adjusted for risk and size, group 1 stocks have higher average returns than group 5 stocks for horizons out 1 year (Black 1973; Copeland and Mayers 1982; and Huberman and Kandel 1987, 1990). Affleck-Graves and Mendenhall (1990) argue however, that the Value Line ranks firms largely on the basis of recent earnings surprises. As a result, the longer-term abnormal returns of the Value Line rankings are just another anomaly in disguise, the post-earnings-announcement drift identified by Ball and Brown (1968), Bernard and Thomas (1989), and others. Stickels (1985) uses event-study methods to show that there is an announcement effect in rank changes that more clearly implies that Value Line has information not reflected in prices. Moreover, Hulbert (1990) reports that the strong long-term performance of Value Line s group 1 stocks is weaker after Over the 6.5 years from 1984 to mid-1990, group 1 stocks earned 16.9% per year compared with 15.2% for the Wilshire 5000 Index. During the same period, Value Line s Centurion Fund, which specializes in group 1, earned 12.7% per year, live testimony to the fact that there can be large gaps between simulated profits from private information and what is available in practice. Regarding professional portfolio management, Jensen (1968, 1969) early results were bad news for the mutual-fund industry. He finds that for the period, 40

52 returns to investors in funds (before load fees, but after management fees and other expenses) are on average about 1% per year below the market line (from the risk free rate through the S&P 500 market portfolio) of the Sharp-Lintner model, and average returns on more than half of his funds below the line. Only when all published expenses of the funds are added back do the average returns on the funds scatter randomly about the market line. Jensen concludes that mutual-funds managers do not have private information. Other studies do not always agree, in tests on 116 mutual funds for the February 1968 to June 1980 period, Henriksson (1984) finds that average returns to fund investors, before load fees but after other expenses, are trivially different (0.02% per month) from the Sharp-Lintner market line. Chang and Lewellen (1984) get similar results for This work suggests that on average, fund managers have access to enough private information to cover the expenses and management fees they charge to investors. Ippolito (1989) provides a more extensive analysis of the performance of mutual funds, he examines 143 funds for the 20-years post-jensen s period , he finds that fund returns, before load fees but after other expenses, are on average 0.83% per year above the Sharp-Lintner market line (from the 1-year Treasury Bill rate through the S&P 500 portfolio). He finds no evidence that the deviations of funds from the market line are related to management fees, other fund expenses, or turnover ratios. Ippolito concludes that his results are in the spirit of the noisy rational expectations model of Grossman and Stiglitz (1980), in which informed investors (mutual fund managers) are compensated for their information costs. Performance evaluation is known to be sensitive to methodology (Grinblatt and Titman 1984). Ippolito (1989) uses the Sharp-Lintner model to estimate normal returns to mutual funds. Brinson, Hood, and Beebower (1980) use passive portfolios meant to match the bond and stock components of their pension funds. We know the Sharp-Lintner model has systematic problems explaining expected returns (size, leverage, E/P, and book-to-market equity effects) that can affect estimates of abnormal returns (Fama, 1991). Elton, Gruber, Das, and Hklarka (1991) test the importance of the Sharp-Lintner methodology in Ippolito s results, they find that during Ippolito s period, his benchmark combinations of Treasury bills with the S&P 500 portfolio produce strong positive estimates of abnormal returns for passive portfolios of non-s&p (smaller) 41

53 stocks-strong confirmation that there is a problem with the Sharp-Lintner benchmarks (also used by Jensen 1968, 1969; Henriksson 1984; and Chang and Lewellen 1984). Elton, Gruber, Das, and Hklarka then use a 3-factor model to evaluate the performance of mutual funds for ; the 3 factors are the S&P 500, a portfolio titled toward non S&P stocks, and a proxy for the market portfolio of Government and corporate bonds. As in Brinson, Hood, and Beebower (1986), the goal of the Elton- Gruber-Das-Hklarka approach is to allow for the fact that mutual funds hold bonds and stocks that are not in the universe covered by the combination of the Treasury bills and the S&P 500 that Ippolito uses to evaluate performance. The Elton-Gruber-Das-Hklarka benchmarks are the returns from passive combinations of Treasury bills with S&P stocks, and bonds. They find that for Ippolito s period, their benchmarks produce an abnormal return on mutual funds of -1.1% per year, much like the negative performance for pension funds (Brinson, Hood, and Beebower 1986). Moreover, unlike Ippolito, but in line with earlier work (Sharp 1966), Elton, Gruber, Das, and Hklarka find that abnormal returns on mutual funds are negatively related to fund expenses (including management fees) and turnover. In short, if mutual and pension fund managers are the informed investors of the Grossman-Stiglitz (1980) model, they are pushing research and trading beyond the point where marginal benefits equal marginal costs (Fama1990). In summary, the investors studied in most detail for private information are pension fund and mutual fund managers. Unlike event studies, however, evaluating the access of investment managers to private information involves measuring abnormal returns over long periods. The tests thus run head-on into the joint-hypothesis problem: measured abnormal returns can result from market inefficiency, a bad model of market equilibrium, or problems in the way the model is implemented. 2-5 Evidence against EMH and alternative models for market behavior The EMH has provided the theoretical basis for the financial market research during seventies and the eighties. In the past, most of the evidence seems to have been consistent with the EMH. Prices were seem to follow a random walk model and the predictable variations in equity returns, if any, were found to be statistically insignificant. While most of the studies in the seventies focused on predicting prices from past prices, 42

54 studies in the eighties also look at the possibility of forecasting based on variables such as dividend yield (e.g., Fama and French 1988), P/E ratios (Campbell and Shiller 1988), and term structure variables (e.g., Harvey 1991). Studies in the nineties look at inadequacies of current asset pricing models. The accumulating evidence suggests that stock prices can be predicted with a fair degree of reliability. Two competing explanations have been offered for such behavior, proponents of EMH (e.g., Fama and French 1995) maintain that such predictability results from time-varying equilibrium expected returns generated by rational pricing in an efficient market that compensates for the level of risk undertaken. Critics of EMH (e.g., Laporta, Lakonishok, Shliefer, and Vishny 1997), argue that the predictability of stock returns reflects the psychological factors, social movements, noise trading, and fashions or fads of irrational investors in a speculative market. The question about whether predictability of returns represents rational variations in expected returns or arises due to irrational speculative deviations from theoretical values has provided the impetus for fervent intellectual inquiries in the recent years. The reminder of this section is motivated largely by this issue, and places greater emphasis on the speculative aspects Market anomalies The EMH became controversial especially after the detection of certain anomalies in the capital markets, these anomalies can be divided into two main categories: longterm return anomalies and calendar effects Long-term return anomalies Fama (1998) provides a review of this literature; many of recent studies on longterm returns suggest market inefficiency, specifically, long-term underreaction or overreaction to information. Fama (1998) gives a solid no as an answer to the question whether this literature (long-term return anomalies) viewed as a whole suggests that efficiency should be discarded, and he gives two reasons for his answer: First, an efficient market generates categories of events that individually suggest that prices overreact to information, but in an efficient market, apparent underreaction will be about as frequent as overreaction. If anomalies split randomly between underreaction and overreaction, 43

55 they are consistent with market efficiency. Second, long-term return anomalies are sensitive to methodology, they tend to become marginal or disappear when exposed to different models for expected (normal) returns or when different statistical approaches are used to measure them. Thus, even viewed one-by-one, most-long term return anomalies can be reasonably be attributed to chance (Fama 1998) Overreaction and underreaction One of the first papers on long-term return anomalies is DeBondt and Thaler (1985). They find that when stocks are ranked on three-to five- year past returns, past winners tend to be future losers, and vise versa. They attribute these long-term return reversals to investors overreaction. In forming expectations, investors give too much weight to the past performance of firms and too little to the fact that performance tends to mean-revert. DeBondt and Thaler seem to argue that overreaction to past information is a general prediction of the behavioral decision theory of Kahneman and Tversky (1982). Thus, one could take overreaction to be the prediction of a behavioral finance alternative to market efficiency. Lakonishok et al. (1994) argue that ratios involving stock prices proxy for past performance, firms with high ratios of earnings to price (E/P), cash flow to price (C/P), and book-to-market equity (BE/ME) tend to have poor past earning growth, and firms with low E/P, C/P, and BE/ME tend to have strong past earnings growth. Because the market over-reacts to past growth, it is surprised when earnings growth mean reverts. As a result, high E/P, C/P, and BE/ME stocks (poor past performers) have high future returns, and low E/P, C/P, and BE/ME stocks (strong past performers) have low future returns. If apparent overreaction was the general result in studies of long-term returns, market efficiency would be dead, replaced by the behavioral alternative of DeBondt and Thaler (1985). In fact, apparent underreaction is about as frequent, the granddaddy of underreaction events is the evidence that stock prices seem to respond to earnings for about a year after they are announced (Ball and Brown 1983; Bernard and Thomas 1990). More recent is the momentum effect identified by Jagadeesh and Titman (1993); stocks with high returns over the past year tend to have high returns over the following three to six months. Over recent event studies also produce long-term post-event abnormal return 44

56 that suggest underreaction. Custias et al. (1993) find positive post-event abnormal returns for divesting firms and the firms they divest. They attribute the result to market underreaction to an enhanced probability that, after a spinoff, both the parent and the spinoff are likely to become merger targets, and the recipients of premiums. Desai and Jain (1997) and Ikenberry et al. (1996) find that firms that split their stock experience long-term positive abnormal returns both before and after the split, they attribute the postsplit returns to market underreaction to the positive information signal of by split. Finally, Michael et al. (1995) find that stock prices seem to under-react to the negative information in dividend omissions and the positive information in initiations IPOs and SEOs Among the more striking of the long-term return anomalies is the study of initial public offerings (IPOs) and seasonal equity offerings (SEOs) by Loughran and Ritter (1995). They find that the total wealth generated at the end of five years if one invests $1 in each IPO or SEO immediately following the event is about 70% of that produced by the same buy-and-hold strategy applied to a sample of stocks matched to the IPOs and SEOs on size. IPOs and SEOs clearly have poor long-term returns during the Loughran- Ritter sample period ( ). The interesting question is whether the returns are really abnormal or whether they are shared with non-event firms similar on characteristics related to average returns, during the Loughran-Ritter period, variables known to be related to average stock return include size and book-to-market equity (Fama and French, 1992), and short-term past return (Jagadeesh and Titman, 1993). Since the long-term buy-and-hold returns in Loughran and Ritter only control for size, their results might be affected by other variables that are systematically related to average return. Following up on this possibility, Brav and Gompers (1997) compare five-year buy-and-hold returns on IPOs with the returns on portfolios that match the IPOs on size and book-to-market equity (BE/ME) but exclude SEOs as well as IPOs. The five-year wealth relative (the ratio of five-year buy-and-hold wealth for IPOs to five-year buy-andhold wealth for the benchmarks) rises from about 0.7 with the Loughran-Ritter size benchmarks to a bit more than 1.0 (that is the anomaly disappear) when the benchmarks control for BE/ME as well as size. 45

57 Similarly, Brav et al. (1995) find that the five-year buy-and-hold returns on SEOs are closed to those of non-event portfolios matched on size and BE/ME, Brav (1997) and Michell and Stafford (1997) show that IPOs and SEOs are typically small growth stocks, Fama and French (1993) show that such stocks have low returns during the post-1963 period. The results of Brav and Gompers (1997) and Brav et al. (1995) then suggest that explaining the IPO-SEO anomaly reduces to explaining why small growth stocks in general have poor returns during the IPO-SEO sample period. In other words, if there is a mispricing problem, it is not special to IPO-SEO stocks (Fama, 1998). Fama (1998) argues that the results for IPOs and SEOs do not imply that benchmark matching on size and BE/ME is always superior to estimating abnormal returns, he also says that all methods for estimating abnormal returns are to bad-model problems, and no method is likely to minimize bad-model problems for all classes of events. The important general message from the IPO-SEO results is one of caution: two approaches that seem closely related (both attempt to control for variation in average returns related to size and BE/ME) can produce much different estimates of long-term abnormal-returns Mergers Asquith (1983) and Agrawal et al. (1992) find negative abnormal returns for acquiring firms up to five years following merger announcement. Using a comprehensive sample of mergers for , Mitchell and Stafford (1997) also find negative longterm abnormal returns for acquiring firms. They find that the three-year post event buyand-hold return for equal-weighted acquiring firms is on average 4% lower than for portfolios matched to acquiring firms on size and BE/ME. In economic terms, this is not a dramatic anomaly. For formal inferences, Mitchell and Stafford (1997) estimate three factor model on the monthly returns on a rolling portfolio that includes firms with acquisitions during the preceding three years, when the acquirers are equal-weighted, the intercept, that is, the average monthly abnormal returns for the three years after a merge is -0.25% per month (-25 basis points, t = -3.49). When acquiring firms are valueweighted, the intercept of the equation drops to -0.11% per month (t = -1.55). Thus, if there is an anomaly, it is more important for smaller acquiring firms. They show that 46

58 abnormal post-announcement average returns to acquiring firms are limited to mergers financed with stocks, that is, mergers that are also SEOs. When mergers are financed without issuing stocks, the negative abnormal post-event returns disappear. This suggests that there is no distinct merger anomaly; any merger anomaly may be the SEO anomaly in disguise Stock splits Desai and Jain (1997) and Ikenberry et al. (1996) find that for 17-year period, stock splits are followed by positive abnormal returns of about 7% in the year after the split. Abnormal returns are calculated relative to benchmarks that control for size, BE/ME, and, in Desai and Jain, past one-year return. To test whether such an anomaly is real or the sample-specific results of chance is to examine a different sample period; Fama et al. (1969) examine splits during the 33-year period. They find no drift in cumulative abnormal returns during the 30 months following splits, since the split anomaly fails the out-of sample test provided by Fama-Fisher-Jensen-Roll, it seems reasonable to conclude that the anomaly is not real, unless the market has recently becomes inefficient (Fama 1998) Self-tenders and share repurchases Lakonishok and Vermaelen (1990) examine long-term returns following selftender offers (tenders by firms for their own shares) during period. Ikenberry et al. (1995) examine long-term returns following share repurchases during the period. Mitchell and Stafford (1997) study both self-tenders and repurchases for the period, they find that three-year post-event buy-and-hold abnormal returns, computed relative to matching portfolios that control for size and BE/ME, are 9% for self-tenders (475 events) and 19% for much larger sample of 2542 repurchases. When they estimate the three factor regression for monthly returns on an equal-weight portfolio that contains all self-tenders and repurchases in the last three years, however, the average abnormal return is 0.11% per month (t = 1.62). Any hint of significance, economic or statistical, disappears entirely when the stocks in the rolling portfolio are value-weighted. The intercept for the value-weight portfolio of self tenders and repurchases is -0.03% (-3 47

59 basis point per month, t = -0.34). Fama (1998) argues that according to theses results, there is no share repurchase anomaly. He adds, that two apparently similar methods for estimating abnormal returns, (i) a matching portfolio control for size and BE/ME and (ii) an asset pricing regression that adjusts for sensitivity to risk factors related to size and BE/ME, produce somewhat different results, which illustrates that estimates of long-term abnormal returns can be sensitive to apparently small changes in technique Exchange listings Dharan and Ikenberry (1995) find that during the period, stocks that newly list on the NYSE, or move from Nasdaq to Amex, have negative post-listing abnormal returns. When returns are risk-adjusted using matching portfolios formed on size and BE/ME, the three-year average abnormal return is -7.02%. The t-statistic for this CAR is -2.78, but this is without a full adjustment for the correlation of abnormal returns across firms. Moreover, Dharan and Ikenberry show that the negative post-listing abnormal returns are limited to firms below the NYSE-Amex median in size. Thus, once again, an apparent anomaly is limited to small stocks. Mitchell and Stafford (1997) offer concrete perspective on how significance levels can be overstated because of the failure to adjust for the correlation across firms of post-event abnormal returns. Using the three factor model, they calculate the standard deviations of abnormal returns for portfolios of firms with an event during the most recent 36 months. The proportion vary somewhat through time and cross their three event classes (mergers, share repurchases, and SEOs), but on average the covariances of event-firm abnormal returns account for about half the standard deviation of the event portfolio s abnormal return. Thus, if the covariances are ignored, the standard error of the abnormal portfolio return is too small by about 50%. This estimate need not apply in fact to the exchange listing of Dharan and Ikenberry (1995), but it suggests that a full adjustment for the cross-correlation of post-listing abnormal returns could cause the statistical reliability (t = -2.78) of their -7.02% postevent three-year CAR to disappear. Dharan and Ikenberry s explanation of their negative post-listing abnormal returns is that firms are opportunistic, and they list their stocks to take advantage of the market s overreaction on their recent good times. This explanation seems shaky, 48

60 however, given that any overreaction to past performance has already occurred and will soon be reversed. Moreover, standard signaling theory (e.g., Ross, 1977) does not predict that firms will incur costs to make a false signal whose price effects are soon obliterated. On the contrary, since listing involves costs, it should be a signal that the firm is undervalued Dividend initiations and omissions Michaely et al. (1995) find that during the period, firms that initiate dividends have positive abnormal stock returns for three years after the event, and firms omitting dividends have negative abnormal returns. For the same sample, Brav (1997) finds that the three-year post-event abnormal return following initiations disappears with benchmarks that control for size and BE/ME. Michaely et al. (1995) show that the negative three-year abnormal returns following omissions, confirmed by Brav (1997), are largely concentrated in the second half of their sample period. All this suggests that inferences about long-term returns following changes in dividends should probably await an out-of-sample test. The finding that stock prices under-react to dividend announcements is suspect on other grounds. It seems reasonable that underreaction would occur because the market underestimates the information in dividends about future earnings. However, from Watts (1973) to Benartzi et al. (1997), there is little evidence that changes in dividends predict changes in earnings Spinoffs Cusatis et al. (1993) study the post-events returns of spinoffs 1 and their parents for the period. The benchmarks are firms matched to the event firms on size and industry, and abnormal returns are buy-and-hold abnormal returns (BHARs). Both parents and spinoffs have positive abnormal returns in the three years after the event. The 1 A pure spinoff is defined as a tax-free, pro-rata distribution of shares of a wholly owned subsidiary to shareholders. In both the academic literature and the popular press, spinoffs often consist of various types of distributions of common stock in other companies. These alternative types of distributions include partial as well as full distributions of stock in subsidiaries, taxable and nontaxable distributions, court-ordered as well as voluntary stock distributions, distributions of common shares in publicly traded companies as opposed to subsidiaries, and return of capital distributions. In some cases, a specialized stock distribution such as split offs, and even stock sales such as equity carve outs, are referred to as spinoffs. 49

61 abnormal returns are, however, limited to event firms (parent and spinoffs) acquired in mergers. The conclusion is that the market does not properly assess the increased probability of takeover (and the attended buyout premiums) following spinoffs, the t- statistic for the three-year BHARs for spinoffs range from 0.58 to 2.55, hardly overwhelming. Moreover, in calculating the t-statistics, the BHARs of the event firms are assumed to be independent. It would not take a large adjustment for cross-correlation to produce t-statistics that suggest no real anomaly Proxy contests Ikenberry and Lakonishok (1993) examine stock returns following proxy contests 2 during the period. They find negative post-event abnormal return relative to benchmarks that control for market β and size. In the results for all proxy contests, the post-event abnormal returns are not statistically reliable. The negative postevent returns are only statistically reliable for 50-odd proxy contests in which the dissidents with board representation. Since this result is not an ex ante prediction, the weak evidence for the overall sample seems more relevant, and it does not suggest a reliable anomaly. From the previous review, it appears that if reasonable change in the method of estimating abnormal returns causes an anomaly to disappear, the anomaly is fragile, and it is reasonable to suggest that it is an illusion. Included in this category are IPOs, SEOs, self-tenders, share repurchases, and dividend initiations, other long-term return anomalies are economically or statistically marginal. The negative post-event abnormal returns to acquiring firms in mergers are economically small. For exchange listing, spinoffs, and proxy contests, a full correction for the cross-correlation of long-term post-event abnormal returns could easily reduce them to former anomalies. Whenever value-weight returns are examined, apparent anomalies shrink a lot and typically become statistically unreliable. At a minimum, this suggests that anomalies are largely limited to small stocks. Fama (1998) presents a reasonable alternative explanation, he says that small stocks are just a sure source of bad-model problems. Small stocks always pose problems in tests of 2 The proxy contest is one mean by which shareholders may exercise the control authority embedded in their equity claims. 50

62 asset pricing models, so they are prime candidates for bad-model problems in tests of market efficiency on long-term returns Calendar effects Calendar effects in stock market returns have puzzled financial economists for several years. EMH emphasizes that seasonal patterns should not exist or should only be minor, since their existence implies the possibility of obtaining abnormal returns by making timing strategies. There are many calendar effects exist in the literature, among them the monthly or January effect, the day-of-the-week effect, the trading month effect, holiday effect, and more recently the Halloween effect. Thaler (1987a, 1987b) provides an early and partial survey, while Mills and Couts (1995) provide more recent references January effect Rozeff and Kinney (1976) were the first to document evidence of higher mean returns in January as compared to other months. Using NYSE stocks for the period , they find the average return for January to be 3.48% compared to only 0.42% for the other 11 months. Later studies document the effect persists in more recent years, Bhardwaj and Brooks (1992) for and Eleswarapu and Reinganum (1993) for The effect has been found to be present in other countries as well (Gultekin and Gultekin, 1983). The January effect has also been documented for bonds by Chang and Pinegar (1986). Maxwell (1998) shows that the bond market effect is strong for noninvestment grade bonds, but not for investment grade bonds. More recently, Bharba, Dhillon, and Ramirez (1999) document a November effect, which is observed only after Tax Reform Act of They also find that the January effect is stronger since A number of hypotheses have been suggested in the literature to explain the January effect, among which the tax-loss-selling hypothesis has received most of the recognition. These hypotheses can be grouped into three broad categories. First, are hypotheses centered around measurement problems, particularly studies that focus on the relation between market capitalization and seasonality in the stock market, the argument here is simply that excess return on small firms is postulated to be either a deception caused by poor measurement of the returns on these firms or a compensation for extra 51

63 risk investors bear by holding these stocks (Banz 1981; Brown et al. 1983; Keim 1983; Reinganum 1983; and Roll 1983). The second category comprises hypotheses related to buying pressure at the beginning of the year. These hypotheses provide reasons why individuals and institutions have a grater incentive to sell some of their holdings (particularly small firm s stocks) at the end of the year and repurchase these holdings at the beginning of the following year. Individual investors have more idle cash (from year-end bonuses, holiday gifts and taxloss-selling) at the beginning of the year, which they want to put into the market (Branch 1977; Brown et al. 1983; Constantinides 1984; Chan 1986; Ritter 1988; and Sias and Starks 1997). Meanwhile, institutional mangers engage in a lot of portfolio rebalancing and, or, (window dressing) activities near the end of the year, which makes trades in January of the following year reversals of these activities ( Lakonishok and Smidt 1986; Haugen and Lakonishok 1987; Ritter and Shopra 1989; and Lakonishok et al. 1991). Third are hypotheses related to the seasonality or the timing of information release, because of the coincidental clustering of the calendar year-end and the tax year-end in the USA; Rozeff and Kinney (1976) observed that January sees the release of an unusual amount of accounting information, thus speculating that seasonality is perhaps associated with accounting news. The seasonal difference in the information about the underlying stocks has been examined by some researchers and considered as an alternative explanation for the January effect (Brauer and Chang 1990). Many authors try to obtain evidence for the tax-loss-selling hypothesis by examining stock return patter in countries with different tax codes and tax-year-ends. Significant seasonality was observed in international stock markets but it was not persistent through time in many markets, providing mixed evidence on the tax-lossselling hypothesis. For example, Brown et al. (1983) find that while Australia has similar tax law to the USA, it has a July-June tax year, which gives rise to December-January and July-August seasonal. While the July-August seasonal is consistent with the tax-lossselling hypothesis, the authors conclude that the evidence is inconsistent with tax-lossselling hypothesis because of the existence of December-January seasonality. Hillier and Marshall (2002) reach the same conclusion and reject the tax-loss-selling hypothesis for UK stocks. Using value weighted stock markets in major industrial countries, Gultekin 52

64 and Gultekin (1983) find evidence for a persistent (though generally less significant than that in the USA) January effect in most of the countries, which was construed as support for the tax-loss-selling hypothesis The weekend effect (Monday effect) The first study of weekend effect in security market appeared in the Journal of Business in 1931, written by a graduate student at Harvard named M.J. Fields. He was investigating the conventional Wall Street wisdom at the time that the unwillingness of traders to carry their holdings over the uncertainties of a week-end leads to a liquidation of long accounts and a consequent decline of security prices on Saturday (Fields, 1931, p.415). Fields examines the pattern of the Dow Jones Industrial Average (DJIA) for the period to see if the conventional wisdom was true, he compares the closing price of the DJIA for Saturday with the mean of the closing prices on the adjacent Friday and Monday. He finds, in fact, that prices tend to rise on Saturdays, for the 717 weekends he studies, the Saturday s price was more than $ 0.10 higher than the Friday-Monday mean 52 percent of the time, while it was lower only 36 percent of the time. Cross (1973) studies the returns on the S&P 500 over the period , he finds that the index rises on 62 percent of the Fridays, but only 39.5 percent of the Mondays. French (1980) analysis daily returns of stocks for the period and finds that there is a tendency for returns to be negative on Mondays whereas they are positive on the other days of the week; he notes that these negative returns are caused only by the weekend effect and not by a general closed-market effect. A trading strategy, which would be profitable in this case, would be to buy stocks on Monday and sell them on Friday. Kamara (1997) shows that the S&P 500 has no significant Monday effect after April 1982, yet he finds the Monday effect undiminished from for a portfolio of smaller U.S stocks. Internationally, Agrawal and Tandon (1994) find significantly negative returns on Monday in nine countries and on Tuesday in eight countries, yet large positive returns on Friday in 17 of the 18 countries studied, Steely (2001) finds that the weekend in the UK has disappeared in the 1990s. Numerous factors, might explain the weekend effect. The most logical hypothesis-dubbed calendar time hypothesis by French (1980) is that, prices should rise 53

65 somewhat more on Mondays than on other days because the time between the close of trading on Friday and the close of trading on Monday is three days, rather than the normal one day between other trading days. Accordingly, Monday returns should be three times higher than other weekday returns. French offers an alternative, the trading time hypothesis, which states that returns are generated only during active trading and implies that returns should be the same for every trading day. In any case, neither hypothesis is consistent with the data, another explanation exist in the literature; differences in settlement time of transaction; attitudes of certain investor groups; investor tendency to postpone the announcement of bad news until the weekend so that the market will have time to absorb the stock; and measurement errors Holidays effects In French s investigations of weekend effects he looks at the price behavior after holidays and finds no thing special happening. However, in another early study Fields (1934) finds that the DJIA shows a high proportion of advances the day before holidays. In this case it takes over 50 years for Fields to be resurrected from obscurity by Ariel (1985). Ariel looks at the returns on the 160 days that preceding holidays during the period For an equal weighted index of stocks he finds that the mean return on the pre-holidays was percent, compared to percent on other days, a ratio of grater than 9 to 1. For a value weighted index the pre-holiday returns average percent compared to percent on other days, a ratio of greater than 14 to 1. The differences are both statistically and economically significant. These results were replicated for the 90-year DJIA series by Lakonishok and Smidt (1987). They obtain an average pre-holiday return of percent, compared to normal daily rate of return of percent, ratio of grater than 23 to 1. Many suggestions proposed to explain holidays effects, such as, differences in settlement time of transactions; investors good mood before holidays; and other psychological reasons Turn of the month effect Ariel (1987) examines the pattern of returns within months. For the period he divides months into two parts, the first part starting with the last day of the prior 54

66 month, he then compares the cumulative returns for the two periods using both equalweighted and value-weighted indices. The return for the latter half of the month is negative, all the returns for the period occur in the first part of the month. This result has been replicated by Lakonishok and Smidt (1987). Using 90-year series for the Dow, they find that the return for the four days around the turn of the month, starting with the last day of the prior month, is percent (the average return for a four-day period is percent). Also, the turn-of-the-month four-day return is grater than the average total monthly return which is 0.35 percent. End-of-the-month increases in purchasing power due to salaries; and higher frequency of announcements of companies profit during the first fortnight of the month, have been suggested as explanations for this seasonality The Halloween effect A more recent calendar effect is that described in Bouman and Jacobsen (2002). They find that the return to stocks in 37 countries can be explained almost totally as a result of what they term the Halloween Indicator. They find that the hypothesis of zero mean return to equities in months May-October cannot be rejected. Thus, the old stock market adage Sell in May and Go Away, Don t Come Back Till St.Leger s Day, St.Lerger s day being 2 October, but with the adage generally being seen as a references to the running of the St.Leger race of Doncaster in late September, would seem to be vindicated. They hypothesize that the cause of this may be the taking, by the general economically active public and brokerage community, of significant holidays in summer period. This has the effect of depressing economic and in particular stock market activity in the summer period. Maberly and Pierce (2003) examine the robustness of the Halloween effect to alternative model specifications for Japanese equity prices. They find that the Halloween effect is concentrated in the period prior to the introduction of Nikkei 225 index in September 1986, while the Halloween effect disappear after In addition, Maberly and Pierce (2004) find that Bouman and Jacobsen results are not robust to alternative model specifications for U.S equity prices Other anomalies 55

67 Small firm effect Banz (1981) publishes one of the earliest articles on the small-firm effect which is also known as the size-effect. His analysis of the period reveals that excess returns would have been earned by holding stocks of low capitalization companies. Supporting evidence is provided by Reinganum (1981) who reports that the risk adjusted annual return of small firms was grater than 20 percent. If the market was efficient, one would expect the prices of stocks of these companies to go up to a level where the risk adjusted returns of future investors would be normal, but this did not happen Value-Line enigma The Value-Line organization divides the firms into five groups and ranks them according to their estimated performance based on publicly available information. Over a five year period starting from 1965, returns to investors correspond to the rankings given to firms. That is, higher ranking firms earned higher returns. Several researchers (e.g. Stickel (1985)) find positive risk-adjusted abnormal (above average) returns using Value- Line rankings to form trading strategies, thus challenging the EMH Standard and Poor s (S&P) Index effect Harris and Gurel (1986) and Shleifer (1986) find a surprising increase in share prices (up to 3 percent) on the announcement of stock s inclusion into the S&P 500 index. Since in an efficient market only information should change prices, the positive stock price reaction appears to be contrary to the EMH because there is no new information about the firm other than its inclusion in the index The weather Few would argue that sunshine puts people in a good mood. People in good mood make more optimistic choices and judgments, Saunders (1993) shows that the New York Stock Exchange index tends to be negative when it is cloudy. More recently, Hirshleifer and Shumway (2001) analyze data for 26 countries from and find that stock 56

68 market returns are positively correlated with sunshine in almost all of the countries studied. Interestingly, they find that snow and rain have no predictive power. The last two decades have witnessed an onslaught against the efficient market hypothesis. Yet as Roll (1994) observes, it is remarkably hard to profit from even the most extreme violations of market efficiency. Stock market anomalies are only too often chance events that do not persist into the future. As Fama says consistent with market efficiency hypothesis that the anomalies are chance results, apparent overreaction to information is about as common as underreaction, and post-event continuation of preevent abnormal returns is about as frequent as post event reversal. Most important, consistent with the market efficiency prediction that apparent anomalies can be due to methodology, most long-term return anomalies tend to disappear with reasonable changes in technique (Fama, 198, p. 283). The importance of the efficient markets hypothesis is demonstrated by the fact that apparently profitable investment opportunities are still referred to as anomalies. The efficient market model continues to provide a framework that is widely used by financial economists Volatility tests, fads, noise trading The greatest stir in academic circles has been created by the results of volatility tests. These tests are designed to test for rationality of market behaviour by examining the volatility of share prices relative to the volatility of the fundamental variables that affect share prices. The first two studies applying these tests were by Shiller (1981) and LeRoy and Porter (1981). Shiller tests a model in which stock prices are the present discounted value of future dividends. LeRoy and Porter use a similar analysis for the bond market. These studies reveal significant volatility in both the stock and bond markets. Fluctuations in actual prices greater than those implied by changes in the fundamental variables affecting the prices are inferred by Shiller as being the result of fads or waves of optimistic or pessimistic market psychology. Schwert (1989) tests for a relation between stocks return volatility and economic activity; he finds increased volatility in financial asset returns during recessions which might suggest that operating leverage increases during recessions; he also finds increased volatility in periods where the proportion of 57

69 new debt issues to new equity issues is larger than a firm s existing capital structure. This may be interpreted as evidence of financial leverage affecting volatility. However neither of these factors plays a dominant role in explaining the timevarying volatility of the stock market. The volatility tests of Shiller spawned a series of articles. The results of excess volatility in the stock market have been confirmed by Cochrane (1991), West (1988), Campbell and Shiller (1987), Mankiw, Romer, and Shapiro (1985). The tests have been criticized, largely on methodological grounds, by Ackert and Smith (1993), Marsh and Merton (1986), Kleidon (1986) and Flavin (1983). The empirical evidence provided by volatility tests, suggests that movements in stock prices cannot be attributed merely to the rational expectations of investors, but also involves an irrational component. The irrational behavior has been emphasized by Shleifer and Summers (1990) in their exposition of noise trading. 1. They posit that there are two types of investors in the market: (i) rational speculators or arbitrageurs who trade on the basis of information and (ii) noise traders who trade on the basis of imperfect information. Since noise traders act on imperfect information, they will cause prices to deviate from their equilibrium values. It is generally understood that arbitrageurs play the crucial role of stabilizing prices. While arbitrageurs dilute such shifts in prices, they do not eliminate them completely. Shleifer and Summers assert that the assumption of perfect arbitrage made under EMH is not realistic. They observe that arbitrage is limited by two types of risk: (a) fundamental risk and (b) unpredictability of future resale price. Given limited arbitrage, they argue that securities prices do not merely respond to information but also to "changes in expectations or sentiments that are not fully justified by information". An observation of investors trading strategies (such as trend chasing) in the market provides evidence for decision making being guided by "noise" rather than by the rational evaluation of information. Further support is provided by professional financial analysts spending considerable resources in trying to predict both the changes in fundamentals and also possible changes in sentiment of other investors. Black (1986) also argues that noise traders play a useful role in promoting transactions (and thus, influencing prices) as informed traders like to trade with noise traders who provide liquidity. So long as risk is 58

70 rewarded and there is limited arbitrage, it is unlikely that market forces would eliminate noise traders and maintain efficient prices Models of human behavior In a market consisting of human beings, it seems logical that explanations rooted in human and social psychology would hold great promise in advancing our understanding of stock market behavior. More recent research has attempted to explain the persistence of anomalies by adopting a psychological perspective. Evidence in the psychology literature reveals that individuals have limited information processing capabilities, exhibit systematic bias in processing information, are prone to making mistakes, and often tend to rely on the opinion of others. The damaging attacks on the assumption of human rationality have been spearheaded by Kahneman and Tversky (1986) in their path breaking article on prospect theory. The findings of Kahneman and Tversky have brought into question expected utility theory which has been used descriptively and predictively in the finance and economics literature. They argue that when faced with the complex task of assigning probabilities to uncertain outcomes, individuals often tend to use cognitive heuristics, while useful in reducing the task to a manageable proportion, these heuristics often lead to systematic biases. Using simple decision tasks, Kahneman and Tversky are able to demonstrate consistent decision inconsistencies by manipulating the decision frame. While expected utility theory would predict that individuals would evaluate alternatives in terms of the impact on these alternatives on their final wealth position, it is often found that individuals tend to violate expected utility theory predictions by evaluating the situation in terms of gains and losses relative to some reference point. The usefulness and validity of Kahneman and Tversky's propositions have been established by several replications and extensions for situations involving uncertainty by researchers in the fields of accounting, economics, finance, and psychology. Rabin and Thaler (2001) show that expected utility theory s explanation of risk aversion is not plausible by providing examples of how the theory can be wrong and misleading. They call for a better model of describing choice under uncertainty. It is now widely agreed that the failure of expected 59

71 utility theory is due to the failure to recognize the psychological principles governing decision tasks. The literature on cognitive psychology provides a promising framework for analyzing investors' behavior in the stock market, by dropping the stringent assumption of rationality in conventional models; it might be possible to explain some of the persistent anomalous findings. For example, the observation of overreaction is consistent with the finding that subjects, in general, tend to overreact to new information (and ignore base rates). Also, agents often allow their decision to be guided by irrelevant points of reference, a phenomenon discussed under "anchoring and adjustment". Shiller (1984) proposes an alternate model of stock prices that recognizes the influence of social psychology; he attributes the movements in stock prices to social movements. Since there is no objective evidence on which to base their predictions of stock prices, it is suggested that the final opinion of individual investors may largely reflect the opinion of a larger group. Thus, excessive volatility in the stock market is often caused by social "fads" which may have very little rational or logical explanation. Research into investor behavior in the securities markets is rapidly expanding with very surprising results, again, results that are often counter to the notion of rational behavior. Hirshleifer and Shumway (2001) find that sunshine is strongly correlated with daily stock returns. Using a unique data set of two years of investor behavior for almost the entire set of investors from Finland, Grinblatt and Keloharju (2001) find that distance, language, and culture influence stock trades. Huberman and Regev (2001) provide an example of how and not when information is released can cause stock price reactions. They study the stock price effect of news about a firm developing a cure for cancer. Although the information had been published a few months earlier in multiple media outlets, the stock price more than quadrupled the day after receiving public attention in the New York Times. The efficient market view of prices representing rational valuation of fundamental factors has also been challenged by Summers (1986), who views the market to be highly inefficient. He proposes that pricing should comprise a random walk plus a fad variable, the fad variable is modelled as a slowly mean-reverting stationary process. That is, stock prices will exercise some temporary aberrations, but will eventually return to their equilibrium price levels. 60

72 One may argue that market mechanisms may be able to correct the individual decision biases, and thus individual differences may not matter in the aggregate. However, the transition from micro behavior to macro behavior is still not well established. For example, in their study of price differences among similar consumer products, Pratt, Wise and Zeckhauser (1979) demonstrate the failure of the market to correct individual biases. All arguments aside, the stock market crash of 1987 continues to be problematic for the supporters of EMH, any attempt to accommodate a 22.7 percent devaluation of the stocks within the theoretical framework of EMH would be a formidable challenge. It seems reasonable to assume that the decline did not occur due to a major shift in the perceived risk or expected future dividend. The crash of 1987 provides further credence to the argument that the market includes a significant number of speculative investors who are guided by "non-fundamental" factors. Thus, the assumption of rationality in conventional models needs to be rethought and reformulated. 2-6 Evidence from emerging markets According to International Finance Corporation (IFC) classification criteria, an emerging stock market is the one located in a developing country as defined by the World Bank s GNP per capita criterion. IFC, a leading compiler of emerging market returns, considers the size (as measured by market capitalization) and liquidity (as measured by turn over) in classifying a market as emerging and in deciding to include the securities in the market in its Emerging Market Data Base (EMDB). In addition, inclusion in the EMDB is affected by the industry in which a company operates; the IFC attempts to provide broad coverage of industries important within the market. Thus, a smaller less liquid security might be included, whereas a larger, more liquid one is excluded if the smaller less liquid security represents a particular industry that would otherwise be underrepresented. Although the world (capital) market is neither fully integrated nor completely segmented, there is no doubt that there is increasing interdependence among its segments. Presently, the fully diversified portfolio must consist of a significant portion of foreign securities to mitigate unnecessary risk. Global diversification depends on the correlations among countries. According to the World Bank, a significant degree of correlation between portfolio equity flows and the emerging stock markets development 61

73 indicators has been established. These correlations are enhanced by cross-border investments and improving technology. Moreover, the benefits of diversification are no longer sufficiently achieved through developed markets. While emerging markets in many respects differ from the developed markets, there is still substantial diversity among emerging stock markets in terms of institutional infra structure, market size and liquidity. Emerging markets, especially in the last decades, provide diversification benefits at an increasing rate. There may be several factors to explain the unprecedented development of emerging markets, but it is possible that economic reforms in these markets have left to a rapid increase in equity flows from the industrial to the developing ones. The more established emerging markets of Asia and Latin America and the new capital markets in Eastern Europe, MENA will play a positive role in this process. Most of the studies on EMH are conducted on the world s largest stock markets; the USA, Japan and Europe. In recent years, efficiency in emerging markets has been investigated widely. Researchers have focused on whether these markets are informationally efficient or whether anomalies exist. Barnes (1980) indicates that the Kuala Lumpur stock market is inefficient. While Panas (1990) could not reject market efficiency for Greece. Campbell (1995) examines 20 emerging markets in Latin America, Asia, Middle East, Europe, and Africa. He finds that returns in these emerging markets are more predictable than returns in developed markets and returns are influenced by local rather than global information. Moreover, Antoniou, Ergul, and Holmes (1997) study the Istanbul Stock exchange and find it to be inefficient in the early times and efficiency improves as the country starts liberalization and deregulation. Dickinson and Muragu (1994) find the Nairobi stock market to be efficient. Urrutia (1995), using the variance ratio test, rejects the RWH for the Latin American emerging equity markets of Argentina, Brazil, Chile, and Mexico, whereas the runs test indicates weak form efficiency. In contrast, Ojah and Kermera (1999) find that Latin American equity returns follow a random walk and are generally weak-form efficient. Grieb and Reyes (1999) reexamine the random walk properties of stocks traded in Brazil and Mexico using the variance ratio test and conclude that index returns in Mexico exhibit mean reversion and 62

74 a tendency toward random walk in Brazil. In addition, predictable variations in the emerging market returns have been documented in Bekaert (1995), Harvey (1995b, 1995c) and claessens et al. (1995). Buckberg (1995) also finds evidence of predictability in emerging markets and rejects the hypothesis that lagged price information cannot predict future prices. A low form of predictability in the emerging markets can be viewed as some form of reward for risk taking. The conclusion is that predictability is more likely to be influenced by local information and that lower degrees of predictability in emerging markets can be viewed as a reward for added risk taking. Furthermore, Bailey et al. (1990) present evidence that stock prices of several Asian markets do not follow random walks. Bessembinder and Chan (1995) examine if market participants in the emerging markets take advantage of profit opportunities that may be present due to deviations from the random-walk model. They find that technical rules have some predictive power, but they say technical signals from US markets have stronger forecasting power. Haque et al. (2001) investigate the stability, volatility, risk premiums and persistence of volatility of seven Latin American emerging markets and find that Latin American markets have shown remarkable performance using return to risk measures; predictability seems mixed and has volatility clustering with shocks that decay with time. Haque et al. (2004) study of 10 Asian stock markets suggests that 8 out of 10 Asian markets have returns that are stable over time. The predictability tests suggest most of the Asian emerging markets to be predictable. The non-parametric runs test for weak form of market efficiency decisively rejects the hypothesis for weak form efficiency for all the Asian markets. 63

75 3- The case of Arab stock markets The markets of the Middle East have buzzed with activity since the beginning of western civilization. Yet, these countries of the region that gave the world the basis of modern commerce have been relative latecomers to global financial markets. Where the Middle East is concerned, political and religious issues continue to dominate the headlines. What differentiates the Middle East markets from other emerging markets is the region s heterogeneity. The financial sector in Middle Eastern countries is dominated by commercial banks. The security markets in these countries are relatively small despite the fact that the region contains some of the developing world s largest institutional investors in international markets. Foreign participation, even in the government bond markets, is limited in most countries. Similarly, there have been few direct placements of Middle Eastern equities on foreign markets. Moreover, the use of market-based risk management instruments by countries in the region has been extremely narrow despite the relatively limited degree of export diversification. While there are considerable differences across countries in the importance of equity markets, the supply of corporate securities remains generally limited both in absolute terms and relative to the size of the economies. This reflects several factors that have constrained the demand for and the supply of equities, including the closed, family-owned nature of many companies in the region. Moreover, in several countries public sector enterprises have continued to play a dominant role in a wide range of economic activities. The number of effectively quoted companies thus has been relatively small and the markets have, in general, remained thin. 3-1 Arab stock markets and market efficiency As mentioned in the previous chapter, most of the studies on EMH are conducted on the world s largest stock markets. In recent years, efficiency in emerging markets has been investigated widely. Very few studies, target countries from the Middle East region, most of them concentrated on return predictability and markets integration and linkages. In addition, most of these studies are usually focused on their individual or a small set of countries for a short horizon. 64

76 One early study undertaken by Gandhi et al. (1980) focuses on the Kuwaiti stock market and attempts to measure its efficiency through the use of some empirical tests. The authors find a high correlation in the market index and conclude that the market is inefficient. Bulter and Malaikah (1992) examine individual stock returns in the Kuwaiti and Saudi Arabian markets over the second half of 1980s. Their results indicate market inefficiency in both markets, but significantly more in the Saudi market. However, Alloughani (1995) tests the weak form of EMH in the Kuwaiti stock market by using various methods, both traditional and advanced. The author finds that when using traditional methods, the results provide evidence of weak form efficiency, while when using more recent methods, he obtains opposite results in the sense that the evidence clearly indicates market inefficiency. Another study investigates the Kuwaiti stock market has been done by Al-loughani and Moosa (1997), since they test the efficiency of the Kuwaiti stock market by using a set of moving averages of different lengths. The results obtained by the authors indicate market inefficiency. Additionally, El-Erian and Kumar (1995) examines the RWH in emerging markets by choosing two countries from the Middle East region, Jordan and Turkey, and three other emerging markets from different regions. The study finds that there is serial dependence among the day-to-day price changes in the stock markets of Jordan and Turkey, indicating that the random walk model does not hold for these markets. Similar results, obtained by Omran and Farrar (2002) who reject the RWH for 5 Middle Eastern countries, while Abraham et al. (2002) reject both RWH and weak form efficiency for three Gulf equity markets, Saudi Arabia, Kuwait, and Bahrain, when they use the observed indices, while they cannot refuse them when they correct indices for infrequent trading. Rao and Shankaraiah (2003) find evidence that Bahraini stock market is efficient at the weak form level during the second half of 1990s. Hakeim and Neaime (2002) find evidence that 4 MENA markets Egypt, Jordan, Morocco, and Turkey are mean reversion. Limam (2003) using weekly data, finds long range dependence in eight Arab stock markets, while Omet et al. (2002) reject the weak form efficiency for the Jordanian stock market. On the other hand, Moustafa (2004) finds that 40 stocks out of the 43 including in the UAE index are random, using daily data for the period October 2, 2001 through 65

77 September 1, 2003, while Haque et al. (2004) examine the stability, predictability, volatility, time varying risk premiums and persistence of shocks to volatility for 10 MENA stock markets; including 6 Arab stock markets. They find that 8 out of 10 markets show evidence of volatility clustering, but in 8 MENA stock markets; the shocks are not explosive. Whereas one market shows positive and significant time varying risk premiums. They conclude that MENA equity markets are where investors may find a good return for the investments, since the correlation is found to be low, which provide investors with the opportunity for diversification. Moreover, Al-loughani (2000) studies the relation between large stock and small stock returns in the Kuwaiti stock markets, he finds further evidence on the informational inefficiency of the Kuwaiti market, since in short term large stocks provide a lead in the bull phase. Dahel and Laabas (1999) examine the behavior of stock prices in 4 GCC markets Bahrain, Kuwait, Oman, and Saudi Arabia using weekly data from September 1994 to April They find that Kuwaiti market to be efficient while for the other three markets; weak form of the EMH was rejected based on regression of return test. However, when the sample is split into two, the efficiency hypothesis is not rejected for the second sub period in two of the markets and only by a small margin in the case of the Saudi Arabia. Few researches concentrate on the volatility structure of Arab stock markets. For instance, Dahel (2000) investigates whether Arab stock markets are characterized by excessive volatility of returns. His study includes in addition to 8 Arab stock markets, two emerging and three developed markets. He finds that Arab markets exhibit the lowest level of volatility of returns compared to other emerging and developed markets, and they were not affected by international financial crisis. In addition, Arab stock markets are characterized by low correlations with each other and with international markets. While Hammoudeh and Choi (2004) investigate the volatility of the decomposed stock returns of members of the GCC stock markets into permanent and transitory components using the unobserved-component model with Markov-switching heteroskedasticity (US-MS model). They find that the GCC stock markets vary in terms of sensitivity to the magnitude of return volatility and the duration of volatility. Oman and Saudi Arabia stock markets exhibit extra volatility sensitivity during fad times of the other GCC stock markets, while Kuwaiti, Bahraini, and Saudi Arabia have longer duration of volatility 66

78 during fad times. Moreover, they find that all GCC returns move in the same direction whether in terms of total return, fundamentals or fads under both volatility regimes. They find also that the correlations of the stock returns and their components with each other and with oil price return are also weak, suggesting that country particularities in addition to the oil price return influence the stock component returns. Very few researchers also investigate EMH in Arab stock markets indirectly by examining whether anomalies exist or not Such as calendar effects. For instance, Aly et al. (2004) find no evidence for Monday effect in the Egyptian stock market, using daily data for market index during the period April 26, 1998 to June 6, 2001, while Al-saad and Moosa (2005) find that seasonality is found to take the form of a July effect, as opposed to the better-recognized January effect for the Kuwaiti stock market. They conclude that the finding is attributed to the summer holiday effect. Whereas, Maghayereh (2003) finds no evidence of monthly seasonality and January effect for the Jordanian stock market during the period January 1994 to December Finally, Al-loughani (2003) documents mixed evidence on seasonality regarding the Kuwaiti stock markets. Another line of research examines the properties and characteristics of the Arab stock markets and the prospects and implications of enhanced financial liberalization in the region. It also explores whether these markets can offer international investors unique risk and returns characteristics to diversify international and regional portfolios. Darrat et al. (2000) examine financial integration among three emerging markets in the Middle East region Jordan, Egypt, and Morocco with the U.S market. Using monthly data for the period October 1996 to August 1999, they find that according to Johansen-Juselius cointegration test the Middle East emerging stock markets are segmented globally, but appear highly integrated within the region. The result also indicates that, the Egyptian stock market is a dominant force driving other markets in the region. In the same area, Assaf (2003) using vector auto-regression, investigates the dynamic interactions among stock market returns for 6 GCC countries. His results indicate that there is substantial evidence of interdependence and feed back effects among GCC stock markets, the results also indicate that Bahrain is the dominant market while Saudi Arabia shows slow process in responding to shocks originated in other markets. 67

79 Moreover, Mohd and Hassan (2003) find long term relationship between 3 GCC stock markets Kuwait, Bahrain, and Oman. They also find that information on the price levels is helpful for predicting their changes. In addition to the previous studies, the international diversification benefits between U.S, Turkish, and Egyptian stock markets have been investigated by Maneschiold (2005) who finds that long term relationship at the general index level related to some but not all sub-indices investigated. U.S investors can obtain diversification benefits at a sub-index level. Neaime (2002) studies the liberalization and financial integration for 7 MENA stock markets with international markets. He finds that GCC equity markets still offer international investors portfolio diversification potentials while other emerging MENA stock markets like those of Turkey, Egypt, and Morocco and to lesser extent Jordan have matured and are now integrated with the world financial markets. However, shocks to U.S and UK stock markets are transmitted to the MENA region but not to the GCC stock markets. Girard et al. (2003) investigate relationships between market risk premium, timevarying variance and time-varying covariance in 11 MENA markets and 8 developed markets. They conclude that MENA capital markets are highly segmented and provide diversification benefits to the global investors. From his side, Omran (2003) investigates the impact of real interest rates on stock market activity and liquidity in the Egyptian stock market. He finds that real interest rates have an impact upon stock market performance. Finally, Hammoudeh and Aleisa (2004) study the daily relationships among stock markets of the GCC members, excluding Qatar, and oil prices. They find that the GCC stock markets are candidates for diversified regional portfolios at the country level, while only the Saudi market can predict and be predicted by oil prices. In general, it appears from the previous literature review that relatively less explored area of research has been on the Arab stock markets. With few studies undertaken to date, research on these markets has focused on the issue of efficiency as well as on their integration with international markets. In addition, most of these studies are concentrated on few markets and in many cases only one market and for short horizon of time using monthly and weekly data. 68

80 3-2 The Foundation of Arab stock markets MENA region have been receiving extensive media coverage and public attention for various reasons. Many have recognized that most MENA countries have long abstained from the global trend of further globalization, modernization and political and economic liberalization. Some claim that the region is facing the reduction in oil wealth that can no longer act as a cushion or employ the huge population growth, as a result, Arab countries, as a part of MENA countries, going toward economic and political reforms and increasing the role of the private sector. It is commonly recognized that the availability of financial capital market is a prerequisite for the development and transformation of any nation s economy. Finding and efficiently managing the scarce resources depend on the existing of financial institutions, whether they are banks or nonbank financial institution such as insurance companies, issuing houses and stock markets. Banks mobilize financial resources from the surplus sector of the economy and channel such funds to the deficit units of the economy, whereas the stock market is a market where trading activities for securities take place and its primary function is to allocate resources to the most profitable investment opportunities. Most Arab countries have stock markets which considered as emerging markets. By international standards, Arab stock markets are considered relatively new. Most of them started operating over the last two decades, while others have been in existence for much longer but until recently; there level of activity was not significant. In general, there are significant differences between Arab stock markets characteristics. In this contest, Arab countries can be divided into two groups regarding natural resources: non oil countries, and oil countries. Since the second group mainly constitute of the Gulf Cooperation Council Countries (GCC), which is a customs union that consists of six members, including four major oil-exporting countries, which are important decision makers in the Organization of Petroleum Exporting Countries (OPEC). The six members are Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and United Arab Emirates (UAE). The non-opec members among them are Bahrain and Oman. This section will discuss the foundation of Arab stock markets with some economical background for each country including in this research. 69

81 Egypt Economic reforms in Egypt have faltered due to the post September 11 downturn in tourism, high Suez Canal tolls, and low level of exports. Little progress has been achieved in privatizing or reforming the significantly large public sector. Social concerns have taken precedence as the largest Arab country, with a population of 69 million, suffers from growing unemployment and the need to maintain subsidies on food, energy, and other commodities for the large percentage of the poor. Development of the natural gas export may help the growth of the economy. While investment laws have been revised to promote foreign investments, between 1998 and 2001, FDI actually fell by 50 percent, from approximately US$ 1 billion to 500 million due to bureaucratic constraints. Although decreasing state-owned banking sector still holds the majority of the market share, these banks are characterized by low capitalization, a high percentage of poorly performing loans, massive overstaffing and stifling bureaucracy. The Egyptian legal code is complex and often characterized by lengthy delays. Nevertheless, the legal system protects private property. Regulations and regulatory agencies are influenced by private interests and government corruption, which cause delays in clearing goods through customs, arbitrary decision- making, and high market inefficiencies. The top income and corporate tax are 40 percent. In January 2003, the Egyptian Pound changed from a pegged to a floating exchange rate. Egypt has a long history of financial markets, by the late of 1800s; Egypt had a sophisticated financial structure including a mature stock exchange in both Alexandria and Cairo. The Egyptian stock market has experienced fundamental changes during four major periods from , , , and 1992-present. In the earliest phase, the market was active and growing out at a remarkable rate. By the 1940s, both the Cairo and Alexandria exchanges were very active, and the combined Egyptian stock exchange ranked fifth in the world in terms of overall market capitalization. However, in the second period from , the Egyptian stock market was seriously marginalized by government intervention and restrictions that left it effectively inoperable. In the third period ( ), serious attempts were made to revive the failing stock market to no avail, and the stock exchange continued to stagnate. Finally, in the 1990s (the forth period), the Egyptian stock market went through a significant revival due to government 70

82 liberalization policies. The restructuring of financial markets and privatization programs were key elements in stimulating economic development and capital investment in 1990s. Major changes in the organization of the Egyptian stock exchange took place in January 1997 that significantly reformed the stock market. Today, the stock market once again encompasses the two exchanges at Cairo and Alexandria, both of which are governed by the same regulatory agency, and share a common trading, clearing and settlement system. In addition, several important steps have been taken by the Egyptian government to modernize the stock exchanges. For example, a coherent organization structure with clear division of authority and responsibilities has been created; a new state-of-the-art trading, clearing and settlement system conforming to international standards has been installed; new membership and trading rules have been legislated; and new arbitration and dispute resolution procedures were developed. The Capital Market Authority (CMA) was established in 1990s, as the primary regulatory body for the Egyptian stock exchange, and it is responsible for the issuance of licenses to all financial intermediaries including the central clearing and depository company. The CMA is also responsible for the introduction of any laws and regulations pertaining to the efficiency and transparency of the market. The company Misr Central Clearing and Depository (MCSD) oversees the clearing and settlement of all securities transactions. MCSD is a private company whose primary shareholders are 16 banks, 15 brokerage houses and the stock market exchange itself. Together with CMA, these two agencies work to guarantee that the market functions efficiently and transparency. Egypt s recent economic reforms, mainly the successful implementation of a large privatization program, is often cited as being largely responsible for the rapid growth in Egyptian stock market activities over the last five years. Finally, the Egyptian stock market has been included in the International Finance Corporation s (IFC) composite stock index since January 1997, with a 1 percent weighting in the overall index. Furthermore, Morgan Stanley Capital International covers the Egyptian stock market on a stand alone basis, although it has not yet included Egypt in its benchmark emerging markets index. 71

83 Table 3-1 Some Economic Indicators, Egypt Population (million) GDP (m US$) 97,655 90,285 85,710 81,495 78,491 GDP growth (%) GDP per capita (US$) 1,543 1,397 1,299 1,211 1,143 Inflation rate (%) Source: Uniform Arabian Economic Report 2005, Arab Monetary Fund (AMF) Jordan With scarce economic resources, Jordan s constitutional monarchy has generally been dependent on foreign loans and aid. Legislative and regulatory reforms under king Abdallah II allowed Jordan to accede to the WTO, leading to privatization and economic growth. Although the country faces a heavy dept burden, high unemployment, and the end of Iraqi-subsidized oil, Jordan can bring back tourism and foreign investment by working towards a more peaceful and open Middle East. In 2001, its tariff rate was 13.5 percent. However, the inefficient customs pose a bigger hindrance to imports where they are subject to arbitrary regulations frequent delays. The top income and corporate tax rate in Jordan are 25 and 35 percent respectively. In 2001, the government consumed 23 percent of GDP. While the government promotes foreign investments, investors face numerous obstacles and restrictions such as the minimum capital requirement of $ and a maximum of 49 percent ownership. The 2000 new banking law protects the interests of investors and works against corruption. However, the US Department of State estimates that 30 percent of Jordan s loans are nonperforming. Subsidies still remain for oil, while most price controls have been removed. The judiciary branch is designed to be independent; however the strong executive branch can easily influence the judges in its favor. Similarly, the government is attempting to bring reforms to foster a more competitive environment, yet the bureaucratic and burdensome regulatory system characterizing by red tape and arbitrary application of customs, tax, labor, and other laws is a strong obstacle to attract investments. Regarding the financial equity market, Amman Stock Market (ASM) was formed on 1 January Since its formation, the market has experienced some growth in a 72

84 number of aspects. The ratio of market capitalization to GDP increases from 37 percent in 1978 to about 160 percent in 2004, which indicates the importance of the market in the national economy. However, the market can be seen highly concentrated; since approximately 10 companies in each year accounted for a large proportion of the total trading volume. In other words, most listed shares are thinly traded on the secondary market. The order-driven market making system of the ASM has no designated liquidity providers and orders are prioritized for execution in terms of price and time. By submitting a limit order, a trader provides liquidity for other market participants who demand immediacy. In other words, investors can trade via market orders and consume liquidity in the market. Given the importance of the ASM in the national economy, the Jordanian capital market has seen the introduction of a number of major changes. At the forefront of these changes is the June 2000 implementation of the Electronic Trading System (ETS). This system was bought from the Paris Bourse and its cost (10.5 million French Francs) was funded by the French government. This event can be considered as a qualitative leap because it means more transparency and safety for traders and investors. Since the establishments of the Jordanian capital market, investors have been enjoying a zero tax rate on capital gains and dividends. However, in 1996, the government imposed a 10 percent tax rate on dividends. Table 3-2 Some Economic Indicators, Jordan Population (million) GDP (m US$) 8,460 8,975 9,561 10,160 11,515 GDP growth (%) GDP per capita (US$) 1,755 1,817 1,886 1,954 2,163 Inflation Rate (%) Source: Uniform Arabian Economic Report 2005, Arab Monetary Fund (AMF) Palestine Palestine Stock Exchange (PSE) was incorporated as a private shareholding company in early 1995, with Palestine Development and Investment Company (PADICO) and (SAMED) as its major investors. After the Palestinian National Authority 73

85 (PNA) approved a PADICO-sponsored design and work plan in July 1995, a project team was put together by PSE and entrusted to establish a fully electronic exchange and depository. EFA Software Service, a Canadian company provides both the trading, settlement and clearing systems. By August 1996, the exchange was fully operational and on November 7 th of that year, PSE signed an operational agreement with PNA, allowing for the licensing and qualification of brokerage firms to take place. On February 18 th 1997, PSE conducted its first trading session. 28 shareholding companies have been approved for listing. As a self regulating organization, PSE is charged with enforcing its rule and regulations until 2004, since pursuant to laws # 12 and 13 the supervisory and executive roles have been separated. The first being discharged by a public sector affiliated body, while the second role is being carried out by PSE. Regarding foreign investment, PSE does not impose any restrictions on foreign investment. However, as a result of political problems in Palestine since September 2000; the Palestinian economy suffered a sharp recession. For instance, the unemployment ratio reached 81 percent in While GDP dropped from $ million in 1999 to $ in All these developments affected negatively the investment s environment in Palestine, which in parallel affected the performance of PSE sharply. Table 3-3 Some Economic Indicators, Palestine Population (million) GDP (m US$) 4,442 4,136 3,780 4,222 4,462 GDP growth (%) GDP per capita (US$) 1,378 1,249 1,062 1,135 1,150 Inflation Rate (%) Source: Uniform Arabian Economic Report 2005, Arab Monetary Fund (AMF) Saudi Arabia Saudi Arabia, one of the prominent countries in the Organization of Petroleum Exporting Countries (OPEC), has the largest oil reserves in the world. Oil exports account for 90 percent of export earning, 38 percent of GDP, and 80 percent of the budget revenues. At the same time, the country faces the challenges of a rabidly growing 74

86 population, water shortages, and political challenges from Islamic extremists. Although the government recognizes the need for privatization to reduce its dependence on oil, the transformation will not happen immediately as the private sector constitutes only about 25 percent of the economy. The government imposes subsidies on state-owned industries, resulting in a weighted average annual rate of inflation of percent from 1993 to Furthermore, the government lists sectors that are prohibited to foreign investment, while many others are subject to tedious government regulations in favor of private interest. Its banking sector is tightly controlled by the Saudi central bank and is heavily dependent on the global oil market. While regarding stock market, the Saudi stock market started in 1952 with one company and continued in an unregulated manner till At this point, the central bank (SAMA) took over as a regulatory body, entrusting all trading to take place through commercial banks in the country. In 1990, an electronic trading system was instituted consisting of a central clearing mechanism connected via twelve trading units (CTU) to the twelve commercial banks in the kingdom. Orders for buy and sell have to be entered by bank employees manning these CTUs. Currently 77 companies are listed and eligible for trading, while trading each day is broken into two, two hour sessions, Saturday to Wednesday; and one two our session on Thursday. The minimum tick size is one Saudi Riyal (approximately $ 0.26) and transaction costs starts from a minimum of SR 25. In addition, only limit order are accepted by the system, where the typical order must specify the price and the quantity intended for purchase or sell. A market order would therefore have to taken the form of what is termed a marketable order, where the price is better than or equal to the best bid or offer currently available. The best two bids and offers are publicly visible on the electronic order book, settlement follows the end of the second trading session, and printed certificates are available the next day. 75

87 Table 3-4 Some Economic Indicators, Saudi Arabia Population (million) GDP (m US$) 188, , , , ,558 GDP growth (%) GDP per capita (US$) 9,204 8,725 8,773 9,761 11,122 Inflation Rate (%) Oil reserve (as % of world reserves) Oil production (% of world production) Contribution of oil to GDP (%) Source: Uniform Arabian Economic Report 2005, Arab Monetary Fund (AMF) Kuwait Controlling approximately 9 percent of the world s oil supply, 47 percent of Kuwaiti GDP and 90 percent of its export revenues come from oil production. Reforms by the government have been stalled by political pressure from Islamic and populist parties who benefit from the current system. Similarly, the parliament has delayed Kuwait project to develop oil fields in the northern part of the country due to opposition of allowing foreign investors to gain control of the oil industries. Nevertheless, this project and foreign participation in Kuwait will enable the country to participate in reconstruction in Iraq and serve as a strategic transshipment port for goods bound to the region. Kuwait has no income tax or corporate taxes for wholly owned Kuwaiti companies while foreign corporations are subject to a 55 percent tax rate. The government intervenes in the stock market. Along with the high tax rate, foreign investment faces significant restrictions, such as inability to foreigner to own real state and invest in the oil sector. The banking sector is more competitive and open to foreign investment, although foreigners are restricted to maximum of 49 percent of ownership. Key services that are subsidized by the government are subject to price controls. While no minimum wages exist in the private sector, wages are set in the public sector that employs 93 percent of Kuwaitis In the case of financial market, Kuwait stock market was established in April In August 1982, the official Kuwaiti stock market fell 21 percent in value and the unofficial market fell about 60 percent. From August 1982 until mid-1984, the Kuwaiti 76

88 government bought selected stocks to support prices. In September 1982, it is required that investors in both markets report their open forward positions. At this time, the value of outstanding post-dated checks in both markets was $ 93 billion ($17 billion in the official market and $ 76 billion in the unofficial market) with settlement dates of up to 3 years. The market collapse and ensuring economic and financial crisis are referred to as Al-manakh crisis. Kuwait s response to Al-manakh crisis was to institute laws and regulations governing information disclosure, securities registration, and capital and credential requirements for brokers. As a result, a reorganized Kuwaiti Securities Exchange began trading stocks, bonds and bank deposits in 1984, prices were determined in a competitive auction, trades were conducted by floor brokers on instructions from outside brokers so that floor brokers did not know the identity of their clients. In addition, restrictions on margin trading and short selling were enforced. Table 3-5 Some Economic Indicators,Kuwait Population (million) GDP (m US$) 37,018 34,076 38,111 46,195 55,719 GDP growth (%) GDP per capita (US$) 16,615 15,192 16,128 18,597 21,066 Inflation Rate (%) Oil reserve (as % of world reserves) Oil production (% of world production) Contribution of oil to GDP (%) Source: Uniform Arabian Economic Report 2005, Arab Monetary Fund (AMF) Oman Since the 18 th century, Oman has been governed by an absolute monarchy. In 2001, 66 percent of the government s total revenues came from state-owned enterprises and its ownership of property. The oil industry has grown to be the dominating industry, making up 86 percent of revenues and roughly 47 percent of GDP. At the current rate of production, oil reserves are projected to last for only 18 years. The government realizes that diversification is essential and is trying to respond by expanding its gas-based industry, boosting economic activity, facilitating foreign investment and privatization, 77

89 and promoting private-sector employment. However, due to its constantly changes and complex customs procedures and regulations, Oman s restrictive trade policy is an obstacle to open trade and considerable progress. Similarly, establishing a business in the country can prove to be a tedious process, subject to the approval from various authorities in respect to land acquisition and labor requirements. Lack of clear regulations that explicitly codify Omani labor and tax laws cause ad hoc decisions and complicate the process even further. Burdensome regulatory requirements for approvals cause considerable delays and adverse condition for the private sector. Additionally, political pressures have always influenced the judiciary branch. However, 2001 and 2002 show significant changes in the restructuring of the legal system, where the courts, the public prosecution service, the police and attorney-general have all been separated to function independently. Unemployment remains a significant concern, particularly among the fast-growing young population. To mitigate the problem, the government has implemented a quota program that replaces foreign workers with Omains, which poses another impediment to foreign investment. While individuals do not have to pay an income tax, companies that are 70 percent foreign-owned incur a 30 percent tax, whereas other domestic companies only face a 12 percent tax rate. In addition, foreign ownership above 70 percent requires the approval of the Minister of Commerce and Industry, while certain industries are prohibited in the country all together. With its participation in the WTO, Oman is pressured to open its service sector of foreign firms. The rate of deflation in Oman is another factor of concern for investors, where the weighted annual average was percent from 1993 to The inflationary pressure is kept in check by price controls and a subsidy system. Additionally, the government-operated banking sector approves very favorable loans to Omani citizens. Moreover, Oman s Muscat stock market was established in 1989, with a 122 listed companies and a market capitalization of $ billion in Furthermore, only nationals of the GCC are permitted to invest in the local stock market. 78

90 Table 3-6 Some Economic Indicators, Oman Population (million) GDP (m US$) 19,868 19,949 20,304 21,698 24,824 GDP growth (%) GDP per capita (US$) 8,271 8,050 8,000 9,308 10,965 Inflation Rate (%) Oil reserve (as % of world reserves) Oil production (% of world production) Contribution of oil to GDP (%) Source: Uniform Arabian Economic Report 2005, Arab Monetary Fund (AMF) United Arab Emirates (UAE) The United Arab Emirates (UAE) controls approximately 9 percent of the global oil supply and about 5 percent of the proven natural gas reserves in the world. Oil revenues comprise about one-third of its GDP. Although the energy reserves expected to last for more than 100 years at current rate of production, in recognizing the need for diversification, UAE is focusing on the development of its service sector and non-oil and gas industrial base. Foreign investment and privatization are sought in the interest of modernizing technology and reducing costs; however, foreigners face widespread restrictions in owning land and investing in specific industries. Where the land is not state-owned, private property is generally well protected. Importers are required to have an import license and are subject to various restrictive regulations. Prices on goods are affected through government subsidies. The public sector holds an important role in total employment and provides subsidies services and an extensive welfare system. In 2001, for example, public enterprises in the hydrocarbon sector alone accounted for 59 percent of the government revenues. In providing loan guarantees, the government minimizes the risk of default to attract international investment. The UAE has no income tax, and no other significant taxes. However, foreign banks face 20 percent tax on profits and are subject to quotas to hire UAE nationals, and other restrictions. The UAE stock market is relatively new and small, which contains both official and unofficial markets. The official market started in 2000 and represented two 79

91 government stock markets, Dubai and Abu Dhabi, under the supervision of the Emirates Securities and Commodities Authority. While the unofficial, or OTC, market works through several brokerage firms with most of them affiliated to banks. Since its inception as an unofficial market in the late of 1970s, the UAE stock market has experienced several volatile periods in terms of share trading activity and price level. The period of ( ) had witnessed the creation of many companies due to rising oil prices and the strong interest of the federal government to build a strong national economy. However, the crisis of the Kuwaiti stock market, the crisis of Al-manakh market in 1982, and the falling of oil prices in 1986 had a negative impact on the UAE capital market. The UAE capital market rose again during the period of , due to the establishments of many new companies. While once again, the UAE capital market experienced a deep decline in the summer of 1998 due to several reasons including: lack of regularity, manipulation of the market by block traders and professional investors, negative speculative trading by all participants, lack of financial disclosure, and the drop in oil prices. Since the summer of 1998, the market has suffered sharp declines in both trading volume and trading value to such an extend that the market prices of most traded stocks have decreased under their par value. In response to the stock market crisis in 1998, the UAE government responded by officially recognizing its stock market. The Emirates Securities and Commodities Authorities (ESCA) was established February 1 st, 2000 pursuant to federal law # 4 of 2000 under the chairmanship of the Minister of Economy and Commerce. Its function is to regulate and develop the primary and secondary markets, monitor the operations of the market, and create a favorable environment for investment. As a result, the Dubai Financial Market (DFM) was officially founded in March 2000 as the first organized stock market in UAE. DFM has been trying to increase the investment alternatives available to investors, and sources of financing available to companies, while Abu Dhabi Securities market (ADSM) started operating in November ADSM with its 35 listed companies in 2004 is larger than DFM. However, neither bonds nor mutual funds are yet included. Since the establishments of the official UAE stock market in 2000, it has been growing at the expense of the OTC market. In addition, ESCA enacted a set of statutory orders and regulations that pertain to arbitration, listing, 80

92 brokers practice, disclosure, transparency, financial markets operations, trading, clearance and depository. Moreover, in 2001 ESCA launched an official capital weighted average market index with 1000 points, called Emirates index, consisting of all listing companies. Table 3-7 Some Economic Indicators, United Arab Emirates (UAE) Population (million) GDP (m US$) 70,521 69,546 75,694 88, ,833 GDP growth (%) GDP per capita (US$) 21,719 19,939 20,164 21,964 23,771 Inflation Rate (%) Oil reserve (as % of world reserves) Oil production (% of world production) Contribution of oil to GDP (%) Source: Uniform Arabian Economic Report 2005, Arab Monetary Fund (AMF) Bahrain The Bahraini Stock Exchange (BSE) was established in 1989, BSE has 45 listed companies with a market capitalization of $ 13.5 billion in Electronic trading takes place on the exchange floor facilitated the newly established clearing and settlement house. Furthermore, new legislation allows GCC investors an unrestricted stake, and non- GCC foreign investors up to 49 percent stake in listed companies. Although small by international standard, BSE is positioning it self to be a major player in the Gulf financial markets. The BSE continues to forge ahead with a development strategy aimed at putting Bahrain on the map of the international capital markets. Several initiatives have been launched by the BSE to provide an infrastructure that is modern and similar to systems that are enforced in developed capital markets. The ultimate goal is to enable the BSE to play a pivotal role in the national economy by mobilizing private sector savings and attracting foreign investments, through a truly international market. The ground is being carefully laid for the ultimate opening of the stock market to overseas investors, which will significantly add to Bahrain s ability to attract foreign investment and will develop the financial markets in terms of volatility, market activity, 81

93 depth and liquidity. Another important development concerns disclosure regulations, which have been initiated to enhance transparency and the safety of investors money. A key objective is to attract a large number of small investors. The disclosure regulations are aimed at making more information available on the share prices, performance of listed companies and investors activity. Moreover, the approved disclosure standards are based on the recommendations of IOSCO (International Organization of Securities Commissions) and are similar to the standard applied internationally. In addition, the BSE has also jointed the International Finance Corporations (IFC) Global Index, which is expected to enhance transparency and disclosure in order to create investment opportunities for foreign investors of individuals as well as internationals portfolios. Table 3-8 Some Economic Indicators, Bahrain Population (million) GDP (m US$) 7,970 7,929 8,448 9,606 11,067 GDP growth (%) GDP per capita (US$) 12,492 12,105 12,571 13,922 15,631 Inflation Rate (%) Oil reserve (as % of world reserves) Oil production (% of world production) Contribution of oil to GDP (%) Source: Uniform Arabian Economic Report 2005, Arab Monetary Fund (AMF) 3-3 Economic reforms and development of Arab capital markets The economic reforms in the Arab countries have strengthened the recognition that Arab capital markets play an important role in the economic development process. The role of those markets in meeting financing requirements gained additional importance with the increased reliance by the growing number of member countries on market forces in resource allocation, greater participation of the private sector in the economic activity and for security non-inflationary financing of budget deficits. As a result, Arab capital markets witnessed remarkable developments in their various aspects, including the legal and organization levels, there by contributing to their foundation on sound structures, which are constantly evolving. 82

94 In this aspect, the Arab Monetary Fund (AMF) plays a significant role in developing Arab stock markets. AMF is a regional institution and devised a work program made up of three main components. These were: the conduct of surveys studies aimed at analyzing the regulatory and institutional situation of capital markets in member countries; the establishment of a database to provide information on the activities of those markets; and finally, the provision of technical assistance to Arab countries for the development of markets for financial papers operating in their jurisdiction. Using the data generating by the database on Arab capital markets, AMF establish an AMF index calculated for each market in addition to the market own index. As a result, the bulletin publishes the AMF composite index in the calculation of which the sample shares of all participating markets merged in a single sample. In addition, the Fund started the publication of a quarterly report named Quarterly Bulletin of the Arab Capital Markets Database, the bulletin s first issue was published in April Each issue of this bulletin reports on developments in the participating markets during the relevant quarter. Furthermore, since June 10, 2002, the Fund started to publish on its website, on daily basis, some basic indices for markets participating in the database. Broadly, these developments involved an improvement in the performance of capital markets, a strengthening of their supervision and increased trading on their floors. They are also related to amendments of tax systems, streamlining of administrative procedures, the creation of a favorable environment suited to the requirements of market actors, the introduction of new financial instruments offering a greater variety of investment opportunities, the acceleration and simplification of trading operations, and the promotion of transparency and disclosure. Adding to these was the improvements in skills of operating staff and enhanced discipline and professional ethics, these developments can be summed as follows: - Promotion of the market supervision function A number of Arab countries have been proceeded to separate between the supervisory and executive roles, the first being discharged by a public sector affiliated body, while the second being mostly carried out by the private sector. In this area, most Arab countries enacted capital market laws; aimed at restructuring the markets and 83

95 leading to the separation between the supervisory function, in charge of regulating the issuance and trading of financial paper on the one hand, and the managements of the stock exchange through which such papers are traded and the agency in charge of registering the transfer, sale and purchase of those paper and keeping a registry of records and ownership titles, on the other hand. By end 2005, the separation between the supervisory and executive roles took place in the following Arab capital markets: Jordan, Egypt, Palestine, and United Arab Emirates. While the two roles continue to be simultaneously in the hands of the capital markets itself in the rest of Arab countries. Table 3-9 Market Market Structure for Arab Stock Markets Supervisory and executive roles Existing of primary and Only secondary Duration of settlement are seperated secondary markets market Bahrain No - Yes T+2 Egypt Yes Yes - T+3 Jordan Yes Yes - T+3 Palestine Yes - Yes T+3 Kuwat No - Yes T+3 Saudi Arabia No - Yes T+2 Oman No Yes - T+3 UAE Yes yes - T+3 - Promotion of transparency and disclosure Arab capital markets have been attaching greater importance to the need for increased transparency, and for adapting its exigencies to meet international standards in order to enhance the supervisory role on the one hand, and to ensure equal opportunities for market operators, on the other hand. Accordingly, the scope of instructions, information and data disclosure of which became mandatory, widened. Such information must now include, for example, the names of issuers of financial papers, those of market members, authorized professionals as well as periodic data related to trading movements and main financial indicators. In this connection, most Arab stock exchanges now publish daily, weekly, monthly, and annual bulletins reporting general information on their 84

96 markets and executive boards decisions together with data on traded volumes and price indices. In addition, most of theses markets have signed agreements with world class companies specialized in automated instant reporting on trading, including Reuters and Bloomberg. It is worth noting that these markets are also disseminating their data through the internet, in order to further publicize investment opportunities which they offer. Moreover, the websites of these stock exchanges are now posting daily updated information on trading effected on their floor; together with historical data containing time-series on all data pertaining to exchange activities. On the other hand, these markets have been endeavoring to ensure that joint-stock companies listed on their floors, strictly up-hold the principles of disclosures and transparency. Additionally, to making the submission of annual reports to financial markets authorities mandatory on listed companies, new instructions in some Arab countries are now rendering it an obligation for such companies to present bi-annual and quarterly reports. - Development of the institutional investors role and expansion of investment instruments Most capital markets in the Arab countries have been seeking to develop that role, as a means of enhancing market stability and protecting it from sharp fluctuations. In general, the institutional investor is interested in medium and long-term investment and basis his decision on scientific studies. By contrast, an individual investor seeks to achieve quick capital gains; in view of his limited awareness, his behavior impacts negatively the business of Arab stock exchanges. In this area, authorities in Arab countries have been encouraging long-term savings by creating saving accounts in market-listed shares, which enjoy low capital gain tax; and by authorizing pension funds and insurance companies to deal with those markets. In addition, Arab stock markets encourage the increase of available investment instruments and alternatives such as bonds convertible into shares and investment funds. Since investment funds, which have been established in most Arab capital markets, are being viewed as the most suitable instrument for mobilizing savings and attracting foreign capital. They provide a mechanism for placing resources in financial papers 85

97 carrying differentiated risks and returns, which an individual investor cannot achieve due to his size. These funds enable Arab expatriates and overseas investors to place their savings in Arab markets for financial papers, without having to be physically present in the region. In addition, they offer to foreigners residing in Arab countries the opportunity to enter local financial markets, since the investor s right in those funds is confined to his share in the financial papers in which the resources of the fund are invested, and to a proportionate entitlement to its returns. - Promotion of foreign investments Investments by-laws in most Arab countries have witnessed a number of changes, mostly aimed to attracting foreign investments, meeting the domestic financing requirements, and smoothing the transfer of advanced technologies into their markets. The changes involved, represent part of the steps taken by those countries to open the door for the entry of foreign investments, by removing the obstacles which used to impede their flows. In this context, Arab countries can be divided in two groups. The first includes countries, which do not impose any restrictions on foreign investments in financial papers; these are Egypt, Jordan, and Palestine, while the second group comprises countries, where such restrictions exist in varying degrees; these are the member states of GCC. For instance, in Saudi Arabia and Kuwait, foreigners are allowed to invest in shares through investment funds, the United Arab Emirates allows foreigners to both invest through similar funds; and to own not more than 49 percent in shares of companies whose internal by-laws so permit. However, in Oman, foreigners can buy shares of newly listed companies in proportions of up to 49 percent, and in the case of certain companies, up to 100 percent, while Bahrain allows for GCC nationals, to own up to 100 percent of shares, if it is defined that this will serve the interests of the national economy. Table 3-10 indicates that while GCC stock markets are fully accessible to GCC investors, they have remained relatively closed to international foreign investors, even non-gcc Arab investors, face restrictions on portfolio investment in these stock markets It is expected that, the removal of the various restrictions which faced MENA portfolios flows, will improve and enhance growth and liquidity in these markets; and 86

98 reduce the costs of raising capital in the local market. Although, the open access to foreign investors will contribute significantly to the growth performances of Arab stock markets, this is expected to gradually lower the diversification potentials; that used to be offered to international investors. In addition, increasing financial integration within the Arab countries is expected to bring considerable benefits to Arab investors, since a more liquid capital market, offers lower borrowing costs for Arabian firms wishing to raise funds locally. Moreover, international financial institutions will be willing to diversify their portfolios by tapping the Arab financial markets. Table 3-10 Accessibility of Arab Stock Markets to Foreign Investments Market - open to GCC nationals. - Foreign residents in Bahrain for at least three years, may own up Bahrain to 1% of the capital of 31 listed companies. - Foreigners can trade shares in only 10 of the 45 listed companies and up to 24%. - Unristrected access to foreign investors. Egypt - Repatriation of capital and dividends allowed. - Unristrected access to foreign investors, in specefic sectors foreign investors can hold up to 50% of companies' capital. Jordan - Repatriation of capital and dividends allowed. - open to GCC nationals. Kuwait - Non-kuwaiti residents are allowed to own shares through matual funds only. - Unristrected access to foreign investors. Palestine - Repatriation of capital and dividends allowed. - Open only to GCC nationals who can own up to 25% of listed Saudi Arabia companies other than banks. - Opened recently to foreign investors through matual funds only. - Foreign investors can hold up to 49% of companies' capital. Oman - Repatriation of capital and dividends allowed. - Foreign investors can own up to 49% in companies' capital UAE whose internal by-laws permit. - Amendment of tax systems Many Arab countries subjected their tax systems to thorough amendments directed towards creating incentives, to encourage dealing in financial papers on the one 87

99 hand, and attracting foreign investments, on the other. By virtue of those changes, these countries either reduced, or eliminated taxes on current returns and capital gains arising from dealing in financial papers. It must be noted that no such taxes existed in all Arab countries, which had regular financial markets. Moreover, some Arab countries also, directed their tax reform towards encouraging joint-stock companies; to have their shares listed in their exchanges. - Computerization of dealing systems Most Arab capital markets took vast steps to modernize their dealing systems, and to introduce modern technologies in share trading operations with a view to improve performance, enhance speed and accuracy in conduct of business and increase transparency and operators confidence. As a result, high-tech automated dealing systems were introduced to the markets. Also, some of these markets inaugurated distant-dealing services, which constitute one of the innovation services witnessed by those markets, and offered a mechanism enabling accredited brokers to conclude contracts without the need to be represented on the physical floor, such as Palestine. - Protocols among Arab stock markets Arab stock exchanges have made major strides on the path of cooperation and integration among them selves, by concluding bilateral and trilateral agreements. The thrust of the latter is to increase collaboration between stock exchanges in the areas of financial papers issue and trading, organizing and facilitating clearing and settlement mechanism. These agreements also, aimed at developing cooperation between intermediation institutions in those markets; and encourage joint/cross listing. In that regard, agreements were signed between the stock exchange of Bahrain, Kuwait, and Oman on the one hand, and those between the stock exchanges of Bahrain and Jordan, Abu Dhabi and Palestine, on the other hand. Comparable agreements were also concluded between Kuwait, Lebanon, and Egypt in one case. In addition, a memorandum of understanding was signed in the case of Jordan and Kuwait, while in a third case, an extended agreement was concluded between Abu Dhabi and Khartoum markets for financial papers. 88

100 All those agreements aimed at fostering cooperation and eliminating hurdles hindering the flows of investments between the markets involved. The consolidation of the trend towards greater integration among Arab capital markets; and the preparation of the propitious conditions for upgrading bilateral and trilateral agreements for cross listing to a collective level, call for a high degree of harmonization between the accounting standards followed and legal systems and, particularly, coordination in the area of clearing and settlement. 3-4 The performance of Arab stock markets Most of Arab security markets indices increased at the end of In comparing Arab stock market performance with other international and emerging markets, figure 3-1 presents indices returns between September 2004 and September One can see that except of Bahrain stock market, Arab stock markets performance was better than other international stock markets, while Dubai stock market stands to be the best in performance followed by Palestine stock exchange, which index s return increased with 250 percent during Figure 3-1 Arab Stock Markets Performance Compared to other International Stock Markets 9/2004-9/

101 3-4-1 Market size With respect to market size, Arab stock markets are small by international standards; their total market capitalization constitutes less than 5 percent of the US market and only about 19 percent of that of UK stock market in However, within the group of Arab security markets, their total capitalization value increased dramatically during 2000 and 2005, from $ billion up to $ billion, with a growing rate more than 700 percent (see figure 3-2). It can be seen from figure 3-2 that most market size variables witnessed dramatic changes, market capitalization as a percentage of GDP, increased by 390 percent, while the volume of shares traded increased by 1081 percent. Moreover, the value of traded shares increased with 3924 percent during 2000 and 2005, on the other hand, the number of listed companies decreased by 8 percent, to reach 1467 listed companies in all Arab markets at the end of Figure 3-2 Market Size for Arab Stock Markets between: However, for individual market size and in terms of market capitalization, Saudi Arabia stands to be the largest market in the region at the end of It accounts for about 55 percent of total market capitalization for all Arab stock markets. Followed by Abu Dhabi stock market, while Palestine stock exchange stands to be the smallest among Arab stock markets (see figure 3-3 and table 3-11 respectively). 90

102 Table 3-11 Market Capitalization for Arab Stock Markets (Million US$) Market Abu DHABI , , , , Jordan 4, , , , , , Bahrain 6, , , , , , Saudi arabia 67, , , , , , Kuwait 19, , , , , , Dubai - - 9, , , , Oman 3, , , , , , Egypt 30, , , , , , Palestine , , Total 135, , , , , ,166, Source: Arab Monetary Fund, AMDB Figure 3-3 Relative Market Capitalization to All Markets 2005 The number of listed companies by it self, can provide an indication of the choices of firms available to an investor. In this case, Egypt stands out among Arab markets; with a total number of listed companies reaching 744 companies at the end of 2005 (table 3-12). However, if the number of listed companies is used in conjunction with market capitalization, it will indicate the average market value of listed companies. 91

103 Table 3-12 Total Number of Listed Companies, Market Abu DHABI Jordan Bahrain Saudi Arabia Kuwait Dubai Oman Egypt 1,071 1,110 1, Palestine Total 1,589 1,596 1,710 1,560 1,429 1,467 Source: Arab Monetary Fund, AMDB In this case, Saudi Arabia has by far the highest market value per listed company among Arab markets, at about $ 8391 million followed by Dubai at $ 3733 million, with Egypt having the lowest market value per listed company, after Oman, at $ 107 million. Since for Egypt, over 90 of the 744 companies listed at the end of 2005; are actively traded, while more than 400 companies are classified as closed family corporations, which are listed to qualify for certain tax benefits. Table 3-13 presents market capitalization as a percentage of GDP, which indicates the relative role that a stock market has in the national economy. In this area, it can be seen that the Jordanian stock market has the highest rate of market capitalization as a percentage of GDP at the end of 2004 (160%), followed by Kuwaiti stock market, while Palestine stock exchange has the lowest rate (25%) at the end of Table 3-13 Market Capitalization as a Percentage of GDP Market Abu DHABI % 37.85% 53.44% Jordan 58.54% 70.64% 75.03% % % Bahrain 83.12% 83.25% 91.34% % % Saudi arabia 35.64% 40.00% 39.70% 73.35% % Kuwait 53.62% 78.24% 99.79% % % Dubai % 17.81% 33.80% Oman 17.71% 13.21% 25.94% 33.64% 37.53% Egypt 31.34% 26.92% 31.31% 39.26% 48.51% Palestine 17.25% 17.47% 15.25% 15.41% 24.57% Source: Arab Monetary Fund, AMDB 92

104 3-4-2 Market liquidity Arab stock markets liquidity has been improved during the last years. Market liquidity variables for total Arab stock markets, witnessed significant changes during 2000 and 2005 (see figure 3-4). The total value traded to market capitalization (turnover ratio) increased by 368 percent for total Arab markets, while total value traded to GDP increased with 607 percent. Meanwhile, the average daily trading value increased sharply by 3102 percent. Figure 3-4 Market Liquidity Variables for Arab Stock Markets, However, for individual markets and in the case of the yearly turnover ratio, which is the ratio of yearly trading value to market capitalization at the end of the year, the Saudi stock market is the most active and liquid among Arab stock markets. Its turn over ratio reached 171 percent with average daily trading value $ 3691 million in While the value traded as percentage of GDP reached 189 percent, which puts the Saudi market to be the most active market among Arab stock markets. The Kuwaiti market comes second in market liquidity, with turnover ratio 78 percent; $ 391 million and 93 percent as an average daily trading value and total value traded as a percentage of GDP in 2004 respectively. In addition, Jordanian and Dubai stock markets can be characterized as 93

105 active markets, while Bahraini stock market stands to be the least liquid market among Arab stock markets as they are in 2005 (see tables 3-14, 3-15, and 3-16). Table 3-14 Total Value Traded to Market Capitalization (Turnover Ratio) Market Abu Dhabi % 3.31% 8.02% 21.53% Jordan 8.21% 14.80% 18.83% 23.78% 28.98% 63.25% Bahrain 3.71% 3.79% 2.67% 2.69% 3.43% 4.10% Saudi Arabia 25.78% 30.36% 41.38% % % % Kuwait 21.20% 43.93% 63.03% 91.94% 70.42% 78.53% Dubai % 7.19% 39.14% 98.49% Oman 15.67% 15.94% 11.04% 18.37% 21.31% 27.53% Egypt 38.32% 24.32% 24.46% 15.62% 17.95% 34.87% Palestine 1.70% 0.94% 0.60% 0.76% 19.19% 14.10% source :Arab Monetary Fund, AMDB Table 3-15 Average Daily Trading Value (million US$) Market Abu Dhabi Jordan Bahrain Saudi Arabia Kuwait Dubai Oman Egypt Palestine Total source :Arab Monetary Fund, AMDB 94

106 Table 3-16 Total Value Traded as Percentage of GDP Market Abu Dhabi % 1.25% 4.28% Jordan 4.81% 10.45% 14.13% 26.21% 46.26% Bahrain 3.08% 3.16% 2.44% 2.72% 4.18% Saudi Arabia 9.19% 12.14% 16.43% 74.16% % Kuwait 11.37% 34.37% 62.90% % 93.00% Dubai % 1.28% 13.23% Oman 2.78% 2.10% 2.86% 6.18% 8.00% Egypt 12.04% 6.55% 7.66% 6.13% 8.71% Palestine 4.25% 1.90% 1.46% 2.15% 4.49% source :Arab Monetary Fund Database (AMDB) Financial Valuation of Arab Stock Markets Regarding financial valuation of Arab stock markets, Table 3-17 presents a comparison between these markets. Clearly and according to the available data, the most expensive markets at the end of 2005 were those of Saudi Arabia (based on both the P/E and the P/BV ratios) and Jordan (based on P/E ratio), while the least expensive markets were those of Oman (based on P/E ratio) and Bahrain (based on P/BV ratio). Table 3-17 Financial Valuation of Arab Stock Markets, End of 2005 Market P/E ratio P/BV ratio Dividend Yeild (%) Abu DHABI Jordan Bahrain Saudi arabia Kuwait Dubai Oman Egypt Palestine Notes : P/E ratio stands for the price/earning ratio and P/BV for the price/ book value ratio. : (-) data not available. source : Arab Monetary Fund Database (AMDB). 95

107 3-4-4 Market concentration Most Arab stock markets in general suffered from thin trading phenomenon, which indeed affect market liquidity. In other words, most listed shares are thinly traded on the market. Figure 3-5 shows the percentage of the 2 biggest companies share in value traded and market capitalization respectively. It can be seen that in general, Arab stock markets are highly concentrated. In the case of Palestine, the 2 biggest companies share in value traded and market capitalization in 2005 are 81, 76 percent respectively. Which indicate that Palestine stock exchange is highly concentrated. In addition, the percentage of the 2 biggest companies share in value traded and market capitalization for Dubai was 56, 44 percent respectively. The Saudi and Bahraini stock markets also can be characterized to be highly concentrated, since the percentage of the 2 biggest companies share in market capitalization were 38, 31 percent respectively. Figure 3-5 Market Concentration, End of Data description The data that will be used through this research consist of daily prices of Arab stock markets. The time period vary from market to market, but usually run from about 1 st January 1992 to 31 July 2005, the initial and final dates vary from market to market 96

108 due to the establishment date of the market and to the availability of the data, the data was collected piece by piece directly from each stock market. Moreover, all indices used in this study are value weighted indices. The Jordanian stock market index consists of 71 listed companies distributed among 4 sectors at the end of 2005, 33 industrial companies, 11 banks, 9 insurance companies, and 18 service companies, while the Palestinian stock exchange index (Al-Quds index) has 10 listed companies distributed among 4 sectors, 2 industrial, 2 banks, 2 insurance, and 4 service companies. The Egyptian stock market index (CASE 30) has 30 companies. The Saudi index is an all-share index constructed by the central bank, and includes the shares of all listed companies on the Saudi market, the same index structure holds for the UAE, which has two stock markets; Abu Dhabi index which has 59 listed companies and Dubai index with its 30 listed companies. Oman s Muscat stock exchange index has 33 listed companies, of which 13 companies represented the banks and investment companies index sector; 11 represented the industry index sector; and 9 represented the service index sector. Moreover, the Kuwaiti stock exchange index has 35 listed companies, while Bahrain stock market has 25 listed companies. Appendix 1 shows the plot graphs for the natural logarithm and return for each index under examination here, while table 3-18 presents the main descriptive statistics for Arab stock markets indices. The returns are the variables on which we want to focus our attention on, that is R t = 100 * log (P t /P t-1 ), where P t denotes closing price for market index. All the displayed Skewness statistics have asymmetric distributions that are skewed to the right as shown by the positive Skewness statistics, except of Kuwait index which is skewed to the left (negative Skewness). Moreover, kurtosis provides a measure of the thickness of the tails of a distribution relative to the normal distribution. For normal distribution, kurtosis is usually equal to three. The presence of excess kurtosis in the series suggests that the return distributions have a much fatter tail than the normal distribution. Finally, none of the series approximates the normal distribution as shown by the Jarque-Bera statistics. 97

109 Table 3-18 Descriptive Statistics for Daily Market Returns for Arab Stock Markets, R t = 100*log(p t /p t-1 ) Jordan Egypt Palestine Kuwait Saudi Bahrain AbuDhabi Dubai Oman Mean Median Maximum Minimum Std. Dev CV Skewness Kurtosis Jarque-Bera 2,940 10, , ,058,905 18,321, ,850, ,045 Probability Sum Sum Sq. Dev. 1, , , , , , , Observations 3,121 1,666 1, ,082 3, ,098 1,899 98

110 Methodology Are Arab Stock Markets Efficient in the Weak Form Sense of Efficient Market Hypothesis? Estimating the true index correcting for infrequent trading. Regression analysis. Serial correlation test. Non-parametric runs test. Variance test. BDS test for returns independency. Seasonality and calendar effects (day of the week effect, monthly effect, and the Halloween indicator). Findings Arab Stock Markets Are Not Efficient in the Weak Form Sense of Market Efficiency and Do Not Follow RWH. These results are consistent with existing literature regarding emerging markets (Bekaert 1995; Harvey 1995b, 1995c; Claessens et al. 1995; and Buckberg 1995). 99

111 Methodology Is the View of Predictability in Stock Returns (if there is) Related to Whether We Think That These Time Series Are Non-Linear? How Does Thin Trading Affect the Predictability of These Time Series? Estimating the true index correcting for infrequent trading, then reexamine the RW properties. Using logistic map to determine whether non-linearity exists. Testing whether the second moment can characterize the existing non-linearity, through subjecting the residuals of RW and GARCH models to several diagnostic tests. Findings Returns Generating Process in Arab Stock Markets is Non-Linear, while the Second Moment Found to Explain Well the Existing Non-Linearity. 100

112 Methodology Are Arab Stock Markets Characterized By Excessive Volatility of Returns, Relative to Other Emerging And International Stock Markets? GARCH (1,1) model for daily returns of Arab stock markets, and two groups of emerging and developed markets with a total of 15 stock markets. GARCH (1,1) model for weekly data. EGARCH model for daily data. Schewrt model for the three groups Arab, emerging and developed markets to compare the relative volatility. Covariance coefficients. Findings Arab stock markets as a group characterized with a low level of volatility relative to other emerging and developed markets. All Arab markets exhibit volatility clustering except Dubai. Egypt, Kuwait, and Palestine exhibit volatility persistence. Bahrain, Dubai, Kuwait and Oman show signs of leverage effect and asymmetric shocks to volatility. 101

113 4- Testing the efficient market hypothesis for Arab stock markets 4-1 Random Walk Hypothesis (RWH) There are three kinds of random walk, random walk 1, IID increments; random walk 2, independent increments and random walk 3, uncorrelated increments. A useful way to organize the various versions of the random walk and martingale models is to consider the various kinds in dependence that can exist between an asset s returns r t and r t +k of two dates t and t+k. To do this, define the random variables f(r t ) and g(r t +k) while f(.) and g(.) are two arbitrage functions, and consider the situation in which [ ( r ), g( )] 0 cov = f (4-1) t r t+k for all t and for k 0. For appropriately chosen f(.) and g(.), virtually all versions of the random walk and martingale hypothesis are captured by (4-1), which may be interpreted as an orthogonality condition. For example, if f(.) and g(.) are restricted to be arbitrary linear functions, then (4-1) implies that returns are serially uncorrelated, corresponding to the random walk 3 model. Alternatively, if f(.) is unrestricted but g(.) is restricted to be linear, then (4-1) is equivalent to martingale hypothesis. Finally, if (4-1) holds for all functions f(.) and g(.), this implies that returns are mutually independent, corresponding to the random walk 1 and random walk 2 models. The statement that prices, in an efficient market, fully reflect available information, conveys the general idea of what is meant by market efficiency. However, this statement is too general to be tested, encountering a need to develop mathematical models of market equilibrium that would be used in testing market efficiency (Fama, 1965). The random walk model is one of those models; it assumes that successive price changes are independent and identically distributed random variables, so that future price changes cannot be predicted from historical price changes. Hence, the RWH has some testable implications for the weak-form of EMH. 102

114 Several tests have been suggested to test for EMH, the procedures used to test for RWH were chosen on the basis of the implications of EMH. If all relevant and available information is fully reflected in stock price, then: a) Successive price changes will be independent, so that there will be no serial correlation over time between returns; b) Successive price changes will be identically distributed: log (P t ) = log (P t-1 ) + ε t Where ε t is an independent standard random variable, that is; a series of identically distributed random variables with zero mean and variance equal to unity. So the distribution of the changes in stock price must be stationary over time, i.e. stock prices are I(1) while stock returns are I(0). In this research, in addition to the common models used in the literature, we employ the most recent statistical and econometric models. To test for the independence of successive price changes (condition a) we employ runs test, non-parametric tests for detecting the frequency of the changes in the direction of a time series. In addition, estimated serial autocorrelations are performed at various lags to determine whether the autocorrelation between returns is equal to zero. Further, we utilize the Box-Pierce test to determine whether the autocorrelation is equal to zero, based on the sum of squares of the first K autocorrelation coefficients. If the set of auto correlations does not differ from the null set, randomness of returns is implied. However, it is important to test whether successive price changes are identically distributed (condition b). Hence, we use regression analysis, variance ratio, and BDS tests. Moreover, a major difficulty in interpreting the results from tests on thinly traded markets is the confounding effect of infrequent trading on the observed index. Thus rejection of the RWH or the efficient markets hypothesis could simply be a result of having used the observed index. So it is important to take in account the effect of infrequent trading and adjust the observed indices for thin trading Estimating the true index-correcting for infrequent trading Infrequent trading is widespread in most emerging markets and it is particularly so in the case of the markets under examination here. Infrequent trading has two forms: 103

115 The first occurs when stocks are traded every consecutive interval, but not necessary at the close of each interval. This form of infrequency, often dubbed nonsynchronous trading has been studied by Scholes and Williams (1977a, 1977b) and Muthuswamy (1990). Infrequent trading is also said to occur when stocks are not traded every consecutive interval, Fisher (1966), Dimson (1979), Cohen et al. (1978, 1979), Lo and MacKinlay (1990), and Stoll and Whaley (1990b) focus on this non-trading and its consequences. The key to distinguishing nonsynchronous trading from non-trading is the interval over which price changes or returns are computed. When returns are measured on a monthly basis, virtually all stocks will have been traded at least once, but not all stocks will have been transacted exactly at the close of trading on the last trading day of the month, that is nonsynchronous trading. When returns are measured over trading intervals as short as for example fifteen minutes, however, all stocks in the market are unlikely to have been traded at least once in every consecutive fifteen minutes interval, which is nontrading. As the trading interval shrinks, nonsynchronous trading becomes non-trading. The problem is created by the fact that the value of an asset over a certain time cannot be directly observed, if the asset does not trade in that period. Since most indices are computed on the basis of the most recent transaction prices of the constituent stocks, the reported index becomes stale in the presence of infrequent trading; the result is that the observed index does not reflect the true value of the underlying stock portfolio. One of the consequences of infrequent trading is the spurious serial correlation it induces in the observed index returns. Therefore, observed dependence is not necessarily evidence of predictability, but rather may be a statistical illusion brought about by thin trading. A number of different approaches have been suggested to correct for infrequent trading, Stoll and Whaley (1990) use the residual from an ARMA (p,q) regression as a proxy of the true index, whereas; Bassett et al. (1991) propose the use of a Kalman filter to estimate the distribution of the true index, Jokivuolle (1995) suggests a modified version of the Stoll and Whaley approach to estimate the true unobserved index from the history of the observed index, the correction consists of decomposing the log of the observed index into its random and stationary components, using the Beveredge and 104

116 Nelson (1981) methodology, in this; the random component can be shown to equal the log of the true index. To separate the effect of infrequent trading, the approach proposed by Miller, Muthuswamy and Whaley (1994) has been applied. To correct for infrequent trading, this methodology basically suggests that to remove the impact of thin trading, a moving average model (MA) that reflects the number of non-trading days should be estimated and then returns be adjusted accordingly. However, given the difficulties in identifying the non-trading days, Miller et al. have shown that it is equivalent to estimate an AR (1) model from which the non-trading adjustment can be obtained. Specifically, this model involves estimating the following equation: R a a ε t = R t 1 + t (4-2) Using the residual from the regression, adjusted returns are estimated as follows: R adj t = ε ( 1 a ) t 2 (4-3) Where adi R t is the return at time t adjusted for thin trading. Miller, Muthuswamy, and Whaley find thin trading adjustment reduces the negative correlation among returns. The model above assumes that non-trading adjustment is constant over time, while this assumption may be correct for highly liquid markets; it is not the case for emerging markets. Therefore, equation (4-2) will be estimated recursively Regression analysis In a random walk process, the distribution of the changes in stock prices must be stationary over time and the constant term of the stationary series, should be insignificantly different from zero. Consequently, to test for this proposition, we start our analysis with naïve random walk, which is closely associated with weak-form EMH 105

117 P t = P t-1 +ε t (4-4) Where P t = ln (X t ) represents the natural log of the original time series X t ; and ε t is a zeromean pure white noise random variable. If the random walk hypothesis holds, then the series P t will have a single unit root (i.e. will be I(1)) and the series P t (= P t -P t-1 = Ln (X t /X t-1 ) will be purely random. The series P t or R t may be examined further by estimating the equation R t = constant + ε t (4-5) Using ordinary least squares, under the random walk hypothesis, the constant term should be insignificantly different from zero and the resultant residuals should be uncorrelated. Additionally, the following regression will be estimated R a ε t = + R t 1 + t (4-6) Once again if the RWH holds, the constant term and returns lag should be insignificantly different from zero, and ε t to be a white noise random variable Serial correlation (autocorrelation) of the return series Serial correlation (or autocorrelation) test measures the correlation coefficient between a series of returns and lagged returns in the same series. A significant positive serial correlation implies that a trend exists in the series, whereas, a negative serial correlation indicates the existence of a reversal in price movements. A return series that is truly random will have a zero serial correlation coefficients. The beta coefficient from the following regression equation measures the serial correlation of stock i with a lag of K periods: r i, t ai + β iri, t k + ε i, t = (4-7) 106

118 Where r i,t represents the return of stock i at time t; α i is a constant; β i is the lagged return s coefficient; ε i,t represents random error, while k represents different time lags. The serial correlation test assumes normal distribution for the stock price changes (returns). The null hypothesis to be tested is that no significant correlation exists between price changes, i.e. β 1 = β 2 =...= β j =0. Since random walk 1 implies that all autocorrelations are zero, a simple test statistic of random walk 1 that has the power against many alternative hypotheses is the Q-statistics due to Box and Pierce (1970): Q m T m k = 1 ρ 2 ( k) (4-8) under the random walk 1 null hypothesis, it is easy to see that Qˆ = T ˆ ρ 2 ( k) is m m k = 1 asymptotically distributed as 2 χ m. Ljung and Box (1978) provide the following finite- 2 sample correction which yields a better fit to the χ m for small sample size: Q ρ m 2 ' ( k) m T ( T + 2) k = 1 T k (4-9) By summing the squared autocorrelations, the Box-Pierce Q-statistic is designed to detect departures from zero autocorrelations in either direction and at all lags. Therefore, it has power against a broad range of alternative hypothesis to the random walk. However, selecting the number of autocorrelations m requires some care-if too few are used, the presence of higher-order autocorrelation may be missed; if too many are used, the test may not have much power to insignificant high-order autocorrelations. 107

119 4-1-4 Non-parametric runs test A common test for random walk 1 is the runs test, in which the number of sequences of consecutive positive and negative returns, or runs, is tabulated and compared against its sampling distribution under the random walk hypothesis. For example, a particular sequence of 10 returns may be represented by , containing three runs of 1s (of length 1, 3, and 1, respectively) and three runs of 0s (of length 2, 1, and 2, respectively), thus six runs in total. In contrast, the sequence contains the same numbers of 0s and 1s, but only 2 runs. By comparing the number of runs in the data with the expected number of runs under random walk 1, a test of the IID random walk hypothesis may be constructed. To perform the test, we require the sampling distribution of the total number of runs R runs in a sample of n. Mood (1940) was the first to provide a comprehensive analysis of runs. Moreover, the runs test determines whether successive price changes are independent. Unlike its parametric equivalent the serial correlation test, the runs test does not require returns to be normally distributed. A run is a sequence of successive price changes with the same sign, if the returns series exhibit grater tendency of change in one direction, the average run will be longer and the number of runs fewer than that generated by random process. To assign equal weight to each change and to consider only the direction of consecutive changes, each change in returns was classified as positive (+), negative (-), or no change (0). The runs test can also be designed to count the direction of change from any base; for instance, a positive change could be one in which the return is grater than the sample mean, a negative change one in which the return is less than the mean, and zero change representing a change equal to the sample mean. The actual runs (R) are then counted and compared to the expected number of runs (m) under the assumption of independence as given in equation (4-10) below; 108

120 m = N ( N + 1 ) N 3 i = 1 n 2 i (4-10) Where N is the total number of return observations and n i is a count of price change in each category. For a large number of observations (N>30), m approximately corresponds to a normal distribution with a standard error (σ m ) of runs as specified in equation (4-11) σ m = ni ni + N ( N + 1) 2N ni N (4-11) i= 1 i= 1 i= The standard normal Z-statistic (Z=(R-m)/σ m ) can be used to test whether the actual number of runs is consistent with the independence hypothesis. When actual number of runs exceed (fall below) the expected runs, a positive (negative) Z value is obtained. Positive (negative) Z value indicates negative (positive) serial correlation in the return series Variance ratio test A consequence of informational efficiency is that the variance of the increments to the random walk process linearly increases with the sampling interval. Lo and MaCkinlay (1988) proposed a simple specification test for evaluating the random walk properties of asset prices. Specifically, if X t is a pure random walk, the ratio of the variance of the qth difference scaled by q to the variance of the first difference must be unity. A variance ratio that is grater than one suggests that returns series is positively 109

121 serially correlated or that the shorter interval returns trend within the duration of the longer interval. A variance ratio that is less than one suggests that the returns series is negatively serially correlated or that the shorter interval returns tend toward mean reversion within the duration of the longer interval. The variance ratio VR (q) is defined as: 2 σ ( q ) VR ( q ) = (4-12) 2 σ (1) Where σ 2 (q) is 1/q the variance of the q-differences and σ 2 (1) is the variance of the first differences. 2 1 σ ( q) µ nq 2 = ( xi xi q q ˆ) (4-13) m i= q Where: And 14) Where: m = q 1 q nq ( nq q + 1 ) 1 nq 2 σ ( 1) = 1 ( 1) x i x i (4- nq i= 1 ( ) 2 ˆ µ ˆ µ = 1 nq ( x x ) nq 0 They developed test statistics both for homoscedastic and heteroscedastic increments. Because it is the heteroscedasticity in the data that is of interest, we use the more robust heteroscedastic test statistic that uses overlapping intervals. The test statistic is: 110

122 * Z ( q ) VR ( q ) 1 = N ( 0,1 ) (4-15) * [ Φ ( q ) ] 1 2 Where: * Φ ( q ) = q 1 j = 1 2 ( q q j ) 2 δˆ ( j ) And ˆ( δ j) nq 2 ( xi x ˆ i 1 µ ) ( xi j xi j 1 ˆ µ ) i= j+ 1 = nq 2 [( xi xi ˆ 1 µ ) ] i= BDS test for returns independency The null hypothesis for the BDS test (Brock et al., 1987, revised in 1996) is that the data are independently and identically distributed (iid), and any departure from iid should lead to rejection of this null in favor of an unspecified alternative. Hence the test can be considered a broad portmanteau test which has been shown to have reasonable power against a variety of nonlinear data generating processes (see Brock et al., 1991 for an extensive Monte Carlo study). The BDS test statistic is calculated as follows. First, the m m-histories of the data x x, x,..., x ) are calculated for t = 1, 2,, T-m for t = ( t t+ 1 t m+ 1 some integer embedding dimension m 2. The Cointegration integral is then computed, which counts the proportion of points in m-dimensional hyperspace that are within a distance ε of each other: c m, T 2 m m ( ε ) = I ( xt, x s ) ( T m + 1)( T m) ε (4-16) t< s Where I ε is an indicator function that equals one if m x x < ε and zero otherwise, and t m s. denotes the sup. norm. BDS shows that, under the null hypothesis that the observed χ t 111

123 are iid, then c ) m m, T ( ε ) c1 ( ε with probability one as the sample size tends to infinity and ε tends to zero. The BDS test statistic, which has a limiting standard normal distribution, then, follows as: w m, T ( ε ) = T 1 / 2 [ C m, T ( ε ) C σ m, T 1, T ( ε ) ( ε ) m ] Where σ m 1 m m j 2 j 2 2m 2 2m 2 1/ 2 m, T ( ε ) = 2[ K + 2( k C1, T ( ε ) ) ( m 1) C1, T ( ε ) m KC1, T ( ε ) ] j= 1 And K(ε) is estimated by K ( ε ) = m m m 6 h ε ( x t, x s, x r ) t < s < r [( T m + 1)( T m )( T m 1) ] and ( i, j, k) = [ I ( i, j) I ( j, k) + I ( i, k) I ( k, j) I ( j, i) I ( i, k) ]/ 3 h e ε ε ε ε + ε ε Two parameters are to be chosen by the user: the value of ε (the radius of the hypersphere which determines whether two points are close or not), and m (the value of the embedding dimension). Brock et al. (1991) recommend that ε is set to between half and three halves the standard deviation of the actual data and m is set in line with the number of observations available (e.g. use only m 5 for T 500 etc.) 4-2 The volatility of Arab stock markets returns The weak-form of efficient market hypothesis implies that no past realizations should help predict future values, a model that reveals a pattern in the behavior of daily returns violates the weak form of market efficiency. Excessive volatility of stock prices is an important phenomenon to investigate, because of its negative effect on risk-averse investors. In this section, volatility structure of Arab markets returns will be analyzed using several techniques such as GARCH (1,1), EGARCH (1,1), and Schewrt model. 112

124 4-2-1 Generalized autoregressive conditional heteroskedasticity (GARCH) The basic idea behind autoregressive conditional heteroskedasticity ARCH models proposed by Engle (1982) is that, the second moments of the distribution may have an autoregressive structure. Under rational expectations the forecast error is u t+1 = y t+1 -E t (y t+1 ), and the conditional distribution of y t+1 is assumed to be normal with mean µ t+1 and var(y t+1 /Ω t ) = h t+1 = a 0 +a 1 u 2 t, where Ω t is the information set available at time t. However, the ARCH process has a memory of only one period. To generalized this we can start adding lags of u t-1 in the equation h t+1, ι = 1,,q. but then the number of parameters to estimate increases rabidly (Bollerslev 1986). For example, in the GARCH (1,1) model the conditional variance depends on lagged variance terms: h t+1 = a 0 +a 1 +β 1 h t = a 0 +(a 1 + β 1 )h t +a t (u 2 t -h t ) in addition to the lagged u t where u 0 is arbitrarily assumed to be fixed and equal to zero. The parameters can be estimated by maximum likelihood techniques. Conditional on time t information Ω t, (u 2 t -h t ) has a mean of zero, and can be thought as the shock to volatility. The coefficient a 1 measures the extent to which a volatility shock today feeds through into the volatility of the next period, while a 1 + β 1 measures the rate at which this effect dies out over time. Since Engle s seminal work, many generalization of this model have been reported. For example, the GARCH (1,1) with a 1 + β 1 =1 has a unit autoregressive root, so that today s volatility affects forecasts of volatility in to the indefinite future (persistent of volatility), this is therefore known as the integrated GARCH or IGARCH model. Nelson (1991) introduced the Exponential GARCH (EGARCH) model which allows for asymmetric shocks to volatility and tests the leverage effect. The dependence of the second moment in returns captured by the (G)ARCH process is known as volatility clustering, i.e. large changes in price volatility are followed by large changes in either sign. Leverage terms allow more realistic modeling of the observed asymmetric behavior of stock returns according to which a good-news price increase yields lower volatility, while some bad-news decrease in price yields an increase in volatility. For example, when the value of (the stock of) a firm falls, the debt-to-equity ratio increases, which in turn leads to an increase in the volatility of the returns to equity. This suggests that returns could also be described by an autoregression whose residual follows an m th - 113

125 order ARCH-L process, where L stands for the leverage effect (Hamilton and Susmel, 1994). It is also worth mentioning a two-component GARCH which reflects differing short- and long-term volatility dynamics (Ding et al., 1993).The GARCH in mean (GARCH-M) model could be used to capture direct relationships between return and possibly time-varying risk by including the conditional variance in the model for the conditional mean of the variable of interest. Autoregressive Conditional Heteroskedasticity (ARCH) models are specifically designed to model and forecast conditional variances. The variance of the dependent variable is modeled as a function of past values of the dependent variables and independence, or exogenous variables. ARCH models are introduced by Engel (1982) and generalized as GARCH by Bollerslev (1986). GARCH models were found to be extremely useful in economic and finance, because it is very flexible in modeling second moment. If the error term process is 2 ε tv t whereσ v = 1, E(v t )=0 and in the standard h t GARCH (1,1): h t α α ε β h (4-17) 2 = t t 1 Then the sequence {v t } is a white noise process and the conditional and unconditional means of ε t are equal zero. A model for the mean is estimated as: 18) R = β 1 + ε (4- t R t and ε R β R 1 t = t t t In the conditional variance equation written in (4-17), h t is the one-period ahead forecast variance based on past information, it is called the conditional variance. Moreover, the conditional variance equation (4-17) is a function of three terms: The mean, α 0 News about volatility from the previous period, measured as the lag of the 2 squared residual from mean equations ε t 1 (the ARCH term). 114

126 Last period forecast variance h t-1 (the GARCH term). Additionally, the sum of the parameters α 1, β 1 in the conditional variance equation, measures the persistence in volatility and lies between 0 and 1. The (1,1) in GARCH(1,1) refers to the presence of a first-order ARCH term (the first term in parentheses) and a first-order GARCH term (the second term in parentheses). An ordinary ARCH model is a special case of a GARCH specification in which there are no lagged forecast variances in the conditional variance equation. The GARCH models are estimated by the method of maximum likelihood under the assumption that the errors are conditionally normally distributed. For example, for the GARCH (1,1) model, the contribution to the log likelihood from observation t is where l t ' 2 ( y χ y) / σ, = log(2π ) logσ 2 t t t t (4-19) ( y χ ' y) βσ 2 t = ω + α t 1 t 1 + t 1 σ (4-20) This specification is often interpreted in a financial context, where an agent or trader predicts this period s variance by forming a weighted average of a long term average (the constant), the forecasted variance from last period (the GARCH term), and information about volatility observed in the previous period (the ARCH term). If the asset return was unexpectedly large in either the upward or the downward direction, then the trader will increase the estimate of the variance for the next period. This model is also consistent with the volatility clustering often seen in financial returns data, where large changes in returns are likely to be followed by further large changes. There are two alternative representations of the variance equation that may aid in the interpretation of the model: If we recursively substitute for the lagged variance on the right-hand side of the variance equation, we can express the conditional variance as a weighted average of all of the lagged squared residuals: 115

127 ω σ = + α β ε (4-21) 2 t ( 1 β ) j = 1 j 1 2 t j We see that the GARCH (1,1) variance specification is analogous to the sample variance, but that it down-weights more distant lagged squared errors. The error in the squared returns is given byu t = ε σ. Substituting for the variances in the variance equation and rearranging terms we can write our model in terms of the errors: 2 t 2 t 2 ( α + β ) ε 1 + u βv 1 ε ω (4-22) 2 t = + t t t Thus, the squared errors follow a heteroscedastic ARMA (1,1) process. The autoregressive root which governs the persistence of volatility shocks is the sum of α plus β. In many applied settings, this root is very close to unity so that shocks die out rather slowly Exponential generalized autoregressive conditional heteroskedasticity (EGARCH) The EGARCH or exponential GARCH was proposed by Nelson (1991), the specification of conditional variance is 23) log ε 2 2 t 1 2 t 1 σ t = ω + β log σ t 1 + α + γ (4- σ t 1 π σ t 1 ε Note that the left hand side is the log of the conditional variance, this implies that the leverage effect is exponential, rather than quadratic and the forecasts of the conditional variance are guaranteed to be non-negative. The presence of leverage effects can be tested by the hypothesis that γ>0. The impact is asymmetric if γ 0; while ε follows a generalized error distribution. Having estimating the EGARCH model, we will be able to plot a News Impact Curve, since it is often observed that downward 116

128 movements in the market are followed by higher volatilities than upward movements. To account for this phenomenon, Engle and Ng (1993) describe a News Impact Curve with asymmetric response to good and bad news Schewrt model In order to compare the volatility of Arab stock markets with other international and emerging markets, Schewrt approach will be used (Schewrt, 1989). A two-step regression technique is applied to estimate volatility of returns. In the first step, a 13 th - order auto-regression for returns will be estimated R t 13 = β R + i = 1 i t 1 ε t (4-24) The absolute value of the residual from equation (4-24) is an estimate of the standard deviation of the return for t. In the second step, a 13 th -order auto-regression for the absolute values of the errors from equation (4-24) will be estimated 13 ˆ ε t = ρ τ εˆ t i + u t (4-25) i = 1 the fitted values from this second equation, multiplied by (2/π) -1/2, are estimates of the conditional return standard deviation given information available before day t. After the volatility measures are estimated for each market separately, an average measure of volatility is then constructed for each group of markets. This measure is calculated by taking the weighted average of the different market volatilities, with the weights representing the share of each market in the total market capitalization of the group. 4-3 Non-linearity and chaos in stock returns Market efficiency implicitly assumes that investors are rational, where rationality implies risk aversion, unbiased forecasts and instantaneous responses to information. Such rationality leads to prices responding linearly to new information. However, 117

129 emerging markets, especially during the early years of trading, may be characterized by investors who do not have all these attributes. In particular, investors may not always display risk aversion. For example, Benartzi and Thaler (1992) argue that investors may be loss averse, in that they are more sensitive to losses than to gains. Such loss aversion may lead to investors acting in a manner consistent with risk loving or risk neutral behavior. For example, loss-averse investors who have incurred losses may display risk loving behavior in an attempt to recover those losses. In addition, investors may place too faith in their own forecasts introducing bias into their actions (Dabbas et al., 1991; and Fraser and MacDonald, 1993). Similarly, as Schatzberg and Reiber (1992) point out, investors do not always respond instantaneously to information. In particular, uninformed traders may delay their response to see how informed market participants behave, because they do not have the resources to fully analyze the information, or because the information is not reliable. Such examples of investor behavior may result in prices responding to information in a non-linear fashion. There are several reasons why non-linearity may be observed in financial markets. First, the characteristic of the market microstructure may lead to non-linearity because of difficulties in carrying out arbitrage transactions. For example, differing microstructures between stock markets and derivative markets may give rise to non-linear dependence. Stoll and Whaley (1991) show that price discovery takes place in futures market and then the information is carried to the stock market through the process of arbitrage. Delays in transacting the stock market leg of the arbitrage means that the immediate response to the mispricing would only be partial, reflecting the change in the futures price alone. This may induce further arbitrage activity and could actually result in overshooting of the arbitrage bounds. Furthermore, short sales in stock markets may lead to delays in executing arbitrage transactions; this in turn may cause non-linear behavior. Second, non-linearity may be explained in terms of non-linear feed back mechanisms in price movements. When the price of an asset gets too high, self-regulating forces usually drive the price down. If the feed back mechanism is non-linear, then the correction will not always be proportional to the amount by which the price deviates from the asset s real value. Third, non-linearity could arise because of the presence of market imperfections such as transaction costs. Although information arrives randomly to the 118

130 market, market participants respond to such information with a lag, due to transactions costs. In other words, market participants do not trade every time news comes to the market; rather, they trade whenever it is economically profitable, leading to clustering of price changes. Fourth, when announcements of important factors are made less often than the frequency of observations, non-linearity may be observed. For example, monthly money supply announcements will cause non-linearity in daily and weekly series, but not in quarterly series. A fifth reason relates to the fact that, as mentioned above, capital market theory is based on the notion of rational investors. It is assumed that investors are risk averse, unbiased when they set their subjective probabilities and always react to information as it received. The implication is that the data generating process is linear. However, investors may well be risk lovers when taking a gamble in an attempt to recover their losses. Moreover, they may have too much faith in their own forecast, thus introducing bias into their subjective probabilities. In addition, they may not react to information instantaneously, but may delay their response until other investors reveal their preferences. To investigate the existence of non-linearity in return series, the logistic map will be used R t α 0 α R α R + ε (4-26) n = + 1 t 1 + n t 1 t where R t is the return at time t; and n = 2,3. For EMH to be hold, we would expect α 0 =α 1 =α n =0 and ε t to be a white noise process. The main purpose of using this approach is not to determine the precise nature of any non-linearity, but rather to ascertain whether any non-linearity exists. Moreover, evidence of non-linearity per se does not provide an insight into the sources of the non-linearity, or more importantly, the appropriate functional form for the resultant non-linear model. For instance, Siriopoulos et al. (2001) find that the inefficiency observed during the early years of their sample for Athens stock exchange, was manifested through non-linear behavior, while efficiency has been improved with time, according to institutional and regulatory evolution. Interest in non-linear chaotic 119

131 processes has in the recent past, experienced a tremendous rate of development. There are many reasons of this interest, one of which being the ability of such process to generate output that mimics the output of stochastic systems, thereby offering an alternative explanation for the behavior of asset prices. In fact, the possible existence of chaos could be exploitable and even invaluable. If, for example, chaos can be shown to exist in asset prices, the implication would be that profitable, non-linearity-based trading rules exist (at least in the short run and provided the actual generating mechanism is known). Prediction, however, over long periods is all but impossible, due to the sensitive dependence an initial conditions property of chaos. In this contest, the BDS tests the null hypothesis of whiteness (independent and identically distributed observations) against an unspecified alternative using a nonparametric technique. Since the asymptotic distribution of the BDS test statistic is known under the null hypothesis of whiteness, the BDS test provides a direct (formal) statistical test for whiteness against general dependence, which includes both non white linear and non white non-linear dependence. Hence, the BDS test does not provide a direct test for non-linearity or for chaos, since the sampling distribution of the test statistic is not known under the null hypothesis of non-linearity, linearity, or chaos. It is, however, possible to use the BDS test to produce indirect evidence about non-linear dependence [whether chaotic (i.e. non-linear deterministic) or stochastic], which is necessary but not sufficient for chaos. Moreover, the BDS test has reasonable power against the GARCH family of models. However, it is often difficult to disentangle the non-linearity generated by this form of dependence in the second moment, from non-linearity arising as a result of other causes. One solution to this problem is to estimate some form of GARCH for the series R t, such as R t = µ + u t u t N(0,h t ) h t α α u β h = t 1 1 t 1 120

132 the standardized residual, u h 1/ 2 t t, may be subjected to the BDS test and the null hypothesis then becomes one that the specified GARCH model is sufficient to model the non-linear structure in the data against an unspecified alternative that is not. The conclusion being that if the BDS test cannot reject the iid null using appropriate critical values derived from simulation, then the model estimated is assumed to be an adequate characterization of the data. In other words, it has been suggested (for example, Brock et al. 1991, p.19 or p.69) that the BDS test can be used as a general test of model misspecification. In this area, the effectiveness of the BDS test in detecting neglected asymmetries in volatility will be examined. Engle and Ng (1993) and Henry (1998) discus the difficulties in selecting between symmetric and asymmetric GARCH models, the standardized residuals of the GARCH models will be subjected to BDS test to see what is left and whether non-linearity generated by this form of dependence in the second moment or from other causes. 4-4 Seasonality and calendar effects An alternative approach to test the EMH especially the weak form is to test for seasonal patterns or calendar effects in stock returns, since according to the weak form of EMH, stock prices in an efficient market should fluctuate randomly through time in response to the unanticipated components of news. This implies that the future path of price level of an asset is no more predictable than the path of a series of cumulated random numbers. As a result, seasonal patterns should not exist or should be minor, since their existence implies the possibility of obtaining abnormal returns by making time research strategies. In this sequence, three calendar effects will be examined, the day-ofthe-week effect, January effect, and the Halloween effect Day-of-the-week effect The day-of-the-week effect was studied using a model originally proposed by French (1980), the hypothesis to be evaluated is the trading time hypothesis, according to which returns are created only on the working days of the week. This hypothesis is tested using regression with dummy variables, such as 121

133 R t 5 = α D + ε i= 1 i it t (4-27) where R t is the daily logarithmic returns on the general index; D it is a dummy variable taking the value 1 for day i and 0 for all other days (i=1,,5 corresponding to Monday through Friday); α i is the mean return on day i and ε t is an error term assumed to be iid. The hypothesis tested in equation (4-27) is H o :α 1 =α 2 =α 3 =α 4 =α January effect or month-of the-year effect To test for the January effect, the model described by the following equation will be used (Gultekin and Gultekin 1983; Jaffe and Westerfield 1989; Raj and Thurston 1994): R t 12 = α D + ε i= 1 i it t (4-28) here D it takes the value 1 if the return at time t belongs to month i and 0 if it belongs to any other month (i=1,,12 corresponds to January through December); α i is the mean return in month i; and the other variables are defined as in equation (4-27). The hypothesis to be tested in equation (4-28) is H 0 :α 1 =α 2 =...=α 12. Moreover, the mean return in a number of months exceeds the average mean return and that this return would not appear to be a reflection of risk, as reflected in the standard deviations of monthly returns. One can test this formally by examining whether there exists a simultaneous month of the year effect in mean return, and in the standard deviation of these returns. A formal test of the existence of monthly calendar effects in mean returns is given by the ANOVA or Kruskal-Wallis statistics. Let R 2 j be the average rank of observations in the jth group (each month of the year) and n j be the number of observations in the jth group. Then with K groups and N observations in total, the Kruskal-Wallis H statistic is 122

134 H = 12 ( + 1) k R N N j= 1 n 2 j j 3 ( N + 1) (4-29) distributed as χ 2 distribution with N-1 degrees of freedom. A formal test for monthly variation in the second moment is given by the Levene test, which tests the hypotheses H 0 : σ i =σ j i,j, H a : σ i σ j, at least one i,j pair. The test statistic is defined as W k ( N K ) ( Z i Z j ) i= 1 = k N ( K 1) ( Z ij Z i ) i= 1 j= (4-30) where Z ij = Υ ij ~ ~ Υ, Υ i i the median of sup-group i The Halloween effect To test for the existence of the Halloween effect, Bouman and Jacobsen (2002) use regression analysis with dummy variables, which is equivalent to a simple means test, their analysis is represented as: R = µ + α1 + ε (4-31) t s t t where R t represents the continuously compounded index returns defined as the natural logarithm of the price relatives. The dummy variable s t takes on the value 1 if observation t falls in month within the November-April period, and 0 otherwise. The intercept term µ represents the mean return over the May-October period and µ+α 1 represents the mean return over the November-April periods, if α 1 is positive and significant at a meaningful level, then this is considered as an indication of a Halloween effect. However, the unusually large monthly returns documented by Bouman and Jacobson (2002) during November-April periods could be a symptom of the January effect. To test for this 123

135 possibility, equation (4-31) is modified by inserting a second dummy variable J t, which is set to equal 1 whenever month t is a January and 0 otherwise. R µ α s α J + ε t = + 1 t + 2 t t (4-32) 4-5 Empirical results Random walk properties - Regression analysis Table 4-1 and 4-2 show the results of the random walk carried out for the whole period for the observed indices. Table 4-1 indicates that in 6 out of 9 markets, the null hypothesis that the constant term is insignificantly different from zero can not be rejected. While for both Saudi and Bahrain, the null hypothesis can be rejected at the 5% level. Moreover, the results in table 4-1 indicate that for Abu Dhabi, Jordan and Kuwait, the constant term found to be significantly different from zero at all acceptable levels. However, when the return lag has been added to the model but without correction for infrequent trading (table 4-2), the results still the same for both Palestine and Dubai. That is, the coefficients α 0,α 1 found to be insignificantly different from zero, but for Saudi; they found to be significant at 10% level, whereas, the coefficients found to be significantly different from zero for the other markets. Moreover, the RWH indicates that the residual term ε t should be pure white noise error. To test for this property, several diagnostic tests have been used and the results presented in appendix 2 for each model. Table (1) in appendix 2 shows that the residuals from the RW model presented in table 4-1 violate the white noise assumption, since they were serially correlated except Dubai. Since only BDS test found to be significant, this indicates that the residuals are not iid. 124

136 Table 4-1 Random Walk Model for Observed Indices R t = a + ε t Market Coefficient Std.Error t-value P -value Abudhabi Bahrain Dubai Egypt Jordan Kuwait Oman Palestine Saudi Table 4-2 Random Walk Model for Observed Indices R t = a 0 + a 1 R t-1 +ε t Market coefficient std.error t-value P -value Kuwait Egypt Saudi Bahrain Palestine Dubai Jordan Oman Abudhabi a a a a a a a a a a a a a a a a a a Tables 4-3 and 4-4 present the results for returns corrected for infrequent trading. In general, the adjustment of returns to take account of thin trading appears to have 125

137 removed the apparent predictability shown in table 4-1 and 4-2. With one exception for Saudi, Oman, and Palestine (see table 4-4). Since the coefficients α 0,α 1 found to be statistically significant at 5 % level. Furthermore, the diagnostic tests indicate that only the residuals for Dubai, Kuwait, and Egypt are not serially correlated, while all other diagnostic tests were significant, indicating that the residuals do not follow white noise process (see table 5 in appendix 2). While the diagnostic tests for other markets indicate that the residuals are serially correlated. The result for Egypt presented in table 4-1 is consistent with those of Omran (2002) but not for Jordan, since he finds that the return in Jordanian stock market is not predictable when he uses the observed index. Table 4-3 Random Walk Model for Corrected Indices R adj t = a + ε t Market Coefficient Std.Error t-value P -value Abudhabi Bahrain Dubai Egypt Jordan Kuwait Oman Palestine Saudi Serial correlation test Tests for the absence of serial correlation over time between returns were implemented from lag 1 up to lag 30 for both observed and corrected indices. Table 4-5 shows that for the observed indices and on the base of the Box-Pierce test, there is highly significant autocorrelation for all lags at the 1% level except Dubai, implying that the series are not completely random. 126

138 Table 4-4 Random walk Models for Corrected Indices R adj t = a 0 + a 1 R adj t-1 +ε t Market coefficient std.error t-value P -value a Kuwait a Egypt Saudi Bahrain Palestine Dubai Jordan Oman Abudhabi a a a a a a a a a a a a a a a a However, when the corrected indices have been used (table 4-6), the higher P- values for each of Dubai, Egypt, and Kuwait show that we can not reject the hypothesis that the series are random at the 5 % level while the other markets still exhibit autocorrelation in returns even after the adjustment for infrequent trading. These results must be considered under caution, since serial correlation test is a parametric test assuming that return series normally distributed. While none of the series under examination came from normal distribution (see Jarque-Bera statistic in table 3-18). As a result, it is more appropriate to use a non-parametric test such as the runs test. 127

139 Table 4-5 Estimated Autocorrelations for Observed Indices Returns No. Abudhabi Bahrain Dubai Egypt Jordan Lags Q stat. P -value Q stat. P -value Q stat. P -value Q stat. P -value Q stat. P -value No. Kuwait Oman Palestine Saudi Lags Q stat. P -value Q stat. P -value Q stat. P -value Q stat. P -value Notes: Statistic ρ(κ) is the autocorrelation coefficient at lag k, Q(k) is the heteroscedasticity-adusted Box-Pierce Q-test statistic for autucorrelation of order K with asociated P -values. Table 4-6 Estimated Autocorrelations for Corrected Indices Returns No. Abudhabi Bahrain Dubai Egypt Jordan Lags Q stat. P -value Q stat. P -value Q stat. P -value Q stat. P -value Q stat. P -value No. Kuwait Oman Palestine Saudi Lags Q stat. P -value Q stat. P -value Q stat. P -value Q stat. P -value Notes: Statistic ρ(κ) is the autocorrelation coefficient at lag k, Q(k) is the heteroscedasticity-adusted Box-Pierce Q-test statistic for autucorrelation of order K with asociated P -values. 128

140 - Non-parametric runs test The runs test determines whether successive price changes are independent. Unlike its parametric equivalent, the serial correlation test of independence, the runs test does not require returns to be normally distributed. Results of the runs test are shown in table 4-7, both for the observed and corrected indices. Panel A presents the results for the observed indices, the actual number of runs (R) in each of the Arab stock markets can be seen to fall short of the expected number of runs under the null hypothesis of stock returns independence. The resultant negative Z values indicate positive serial correlation. The runs test results show that the successive returns for all Arab stock markets are not independent at 5% level. However, when the indices are corrected for infrequent trading, the results are strikingly different for Egypt, Kuwait, and Saudi Arabia. Since expected and actual number of runs are so close. While for other markets, stock returns dependency still significantly exist even after corrected for thin trading. Based on the corrected indices, we cannot reject weak-form market efficiency for Egypt, Kuwait, and Saudi stock markets. Correcting for infrequent trading in the case of these three markets, leads to absolute reversal in the inference on market efficiency. The results for both Saudi and Kuwait equity markets are consistent with those of Abraham et al. (2002), the same results also obtained by Bulter and Malaikah (1992) when they use the observed indices for Saudi and Kuwaiti markets. Additionally, Haque et al. (2004) find that the RWH can be rejected for both Egypt and Oman but not for Bahrain, Jordan, and Saudi Arabia according to runs test results, when they use weekly data of the observed indices. Moreover, Omran (2002) rejects the RWH for Egypt depending on runs test, when he uses the observed index. 129

141 Table 4-7 Results of Runs Test for Arab Stock Markets, Observed vs. Corrected Indices Abudhabi Dubai Bahrain Jordan Oman Palestine Egypt Kuwait Saudi Panel A:Observed Index Returns Observations( N ) n ( + ) n ( - ) n ( 0 ) Expected runs ( m ) Actual runs ( R ) Standard error ( σm ) Z - statistic a a a a a a a a a Panel B: Corrected Index Returns Observations( N ) n ( + ) n ( - ) n ( 0 ) Expected runs ( m ) Actual runs ( R ) Standard error ( σm ) Z - statistic a a a a a a a Indicates rejection of the null that successive price changes are independentat the 5% level. The runs test tests for a statistically significant difference between the expected number of runs vs.the actual number of runs. A run is defined as a sequence of successive price changes with the same sign. n(+)/n(-)/n(0) represent the number of positive/negative/zero price changes. panel B shows the results for the index, corrected for infrequent trading. 130

142 - Variance ratio test The RWH for each market is tested using the variance ratio test described in section (4-1-5). The variance ratio is computed for multiples of 2, 4, and 8 days, with the one-day return used as the base. Results for the observed and corrected indices are shown in panel A and B of table 4-8, respectively. When the observed indices are used, the RWH is strongly rejected for all markets except Kuwait and Palestine. The variance ratio is not equal unity with the aggregation interval for the stock markets. While for both Kuwait and Palestine, the variance ratio is the same and equal to unity for all multiples. However, when the corrected indices are used, the RWH strongly rejected for all markets even for Kuwait and Palestine. The results for Saudi and Bahrain are different from those obtained by Abraham et al. (2002) since they can not reject the RWH for Saudi and Kuwaiti equity markets, when they implement the variance ratio test for the corrected indices. While the result of the observed indices for Jordan and Egypt are consistent with those of Omran (2002). In addition, the results obtained here for Bahrain, Oman, Kuwait, and Saudi Arabia, contradict the results of Dahel and Laabas (1998) when they use weekly data of the observed indices for these markets and can not reject the RWH using variance ratio test. While Haque et al. (2004) find that Egypt, Bahrain, Oman, and Saudi Arabia show predictability, whereas Jordan shows no signs of predictability when they use weekly data of the observed indices. - BDS test The BDS tests the null hypothesis of whiteness (independent and identically distributed observations) against an unspecified alternative. In other words, the BDS test provides a direct statistical test for whiteness, against general dependence. Tables 4-9 and 4-10 report the BDS test statistics for each market both for observed and corrected indices. The BDS test has been applied for embedding dimensions of m= 2, 3, 4 and 5. For each m, ε is set to 0.5, 1.0, and 1.5 standard deviations (σ) of the data. The iid null hypothesis is overwhelmingly rejected in all cases for the returns series and for all markets even after the indices have been adjusted for thin trading. The results of the BDS test indicate that the iid hypothesis is rejected in favor of an unspecified alternative or 131

143 general dependence, which may includes both non-white linear or non-linear dependence in the time series. Table 4-8 Variance Ratio Estimates and Heteroskedastic Test Statistics for Arab Stock Markets Panel A: variance ratio test for observed index returns Market Abudhabi ** (2.7236) (1.9495) (0.9706) Bahrain ** (8.2478) ( ) ( ) Dubai ** (0.7603) (2.2345) (0.0351) Egypt ** ** (9.7293) (3.1065) ( ) Jordan ** (4.2095) (0.1953) (0.6161) Kuwait (0.2652) (0.2229) (0.4402) Oman ** ** (9.0629) ( ) (3.8216) Palestine ( ) ( ) ( ) Saudi ** ** (6.3285) (2.3956) (1.2535) Panel B: variance ratio test for corrected index returns Market Abudhabi ** ( ) (0.2888) (1.0531) Bahrain ** ** (6.7754) (3.2289) (0.1890) Dubai ** (9.5099) (1.3563) (0.5110) Egypt ** ** (1.1011) (3.0265) (2.2408) Jordan ** ** ** (7.9647) (2.6607) (2.2822) Kuwait ** ** (3.8812) (0.5284) (3.4244) Oman ** ** ** (4.8767) (3.3459) (1.4356) Palestine ** ** (6.5733) (1.6668) (3.0642) Saudi ** (4.8752) (-0.623) (0.5193) ** Indicates rejection of the RWH at the 0.05 level. Figures in parentheses are asymptotic Z statistic (H0:VR(q)=1). The variance ratios are defined as the ratio of (1/q )σ q 2 to σ 1 2 for values of q = 2, 4, and 8, where σ i 2 is the variance of the index return defined as ln(p t /p t- i ).Panel B shows the results for the index, corrected for infrequent trading. 132

144 Table 4-9 BDS Test Results for Observed Return Indices ε/σ m Abudhabi Bahrain Dubai Egypt Jordan BDS Stat. Prob. BDS Stat. Prob. BDS Stat. Prob. BDS Stat. Prob. BDS Stat. Prob ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** Observations ε/σ m Kuwait Oman Palestine Saudi BDS Stat. Prob. BDS Stat. Prob. BDS Stat. Prob. BDS Stat. Prob ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** Observations Notes: The BDS (m,ε) tests for i.i.d., where m is the embedding dimention and ε is distance set in terms of the standard deviation of the data(σ) to 0.5,1.0 and 1.5 standard deviations.** indicates statistical significance at the 5% level. 133

145 Table 4-10 BDS Test Results for Adjusted Return Indices ε/σ m Abudhabi Bahrain Dubai Egypt Jordan BDS Stat. Prob. BDS Stat. Prob. BDS Stat. Prob. BDS Stat. Prob. BDS Stat. Prob ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** Observations ε/σ m Kuwait Oman Palestine Saudi BDS Stat. Prob. BDS Stat. Prob. BDS Stat. Prob. BDS Stat. Prob ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** Observations Notes: The BDS (m,ε) tests for i.i.d., where m is the embedding dimention and ε is distance set in terms of the standard deviation of the data(σ) to 0.5,1.0 and 1.5 standard deviations.** indicates statistical significance at the 5% level. 134

146 4-5-2 Volatility of returns Several methods have been suggested in the literature to test for volatility in stock markets. Regardless of the debate over empirical testing of volatility, the fact remains that volatility is a relative measure. The purpose of this section is to investigate the volatility of Arab stock markets and whether these markets are characterized by excessive volatility of returns, relative to other developed and emerging markets. To this end, in addition to nine Arab stock markets, three emerging and three developed markets will be tested. U.K (FTSE100), USA (S&P 500), and Japan (Nikkei 225) will be used for developed markets, while Israel, Turkey, and India general equity markets indices will be the representative of emerging markets. The data for both emerging and developed markets consists of daily prices and obtained from Yahoo Finance. 3 - Coefficient of variation The coefficient of variation figures presented in table 4-11 measure the degree of volatility of daily market returns relatives. For the group of Arab markets, Oman appears to be, by far, the most volatile followed by Egypt, with Kuwait the least volatile. For the developed markets, the coefficients of variation are higher in average than those for most of the Arab markets, as well as for the developing markets except India. Overall based on the coefficient of variation, the figures seem to indicate that Arab stock markets as a group characterized with a low level of volatility relative to the other two groups. Table 4-11 Coefficient of Variation (C.V) for Daily Returns for the Three Groups Arab stock markets Emerging markets Developed markets market C.V % market C.V % market C.V % Abudhabi % India % Japan % Bahrain % Israel % UK % Dubai % Turkey % USA % Egypt % Total % Total % Jordan % Average 41.4 Average 48.1 Kuwait % Oman % Palestine % Saudi % Total % Average Although all Arab markets are emerging markets, the distinction between Arab and emerging markets is made only for the purpose of the analysis. 135

147 -Schwert measure In the case of the coefficient of variation, volatility in Arab, emerging, and developed markets has been investigated at the market level. The figures in table 4-11 do not provide a clear assessment of the degree of volatility of returns in Arab markets as a group, compared to that in other two groups of markets. The Schwert measure of volatility used at the group level should reveal not only the potential trends in volatility of returns in Arab markets, but also their level of volatility relative to that of emerging and developed markets. Figure 4-1 shows the Schwert measure of volatility for the 3 groups. The main observation that could be made from the figure is that, Arab stock markets as a group exhibit the lowest level of volatility while emerging markets found to be the highest. However, Arab markets show a remarkable increase of volatility, particularly over the period 2003 corresponding to the invasion on Iraq. 4 Figure 4-1 Markets Volatility (Schwert Model) 4 The end of the year 2004 has been used to calculate the weights. 136

148 - GARCH (1,1) models The volatility and persistence of shocks to volatility for all markets, including emerging and developed markets, are presented in table Based on empirical investigation conducted by GARCH (1,1) for daily data, one finds that only Dubai does not show any volatility clustering. In other words, there is evidence of significantly volatility clustering in 8 out of 9 Arab markets. ARCH parameter (α 1 in table 4-12) is less than unity for all fifteen markets, signifying that shocks are not explosive. Economic interpretations of the ARCH effect in stock returns have been provided within both micro and macro frameworks, according to Bollerslev et al. (1992) and other studies. The ARCH effect in stock returns could be due to clustering of trade volumes, nominal interest rate, dividends yields, money supply, oil prices etc. Moreover, the persistence of shocks is measured by (α 1 +β 1 ) in the GARCH model. According to Engel and Bollerslev (1986), if α 1 +β 1 = 1 in GARCH model, a current shock persists indefinitely in conditioning the future variance. Since the sum α 1 +β 1 represents the change in response function of shocks to volatility persistence, a value greater than unity implies that response function of volatility increases with time and a value less than unity implies that shocks decay with time (Chou, 1988). The closer to unity is the value of persistence measure; the slower is the decay rate. The findings here reveal that in three Arab stock markets (Egypt, Kuwait, and Palestine), one fails to reject that α 1 +β 1 = 1, i.e. shocks to volatility are permanent. It implies that the conditional variance is non-stationary. The volatility movements affects the stock markets of these three countries, volatility is persistent and has a slow rate of decay. On the other hand, Oman exhibits an increasing response function of volatility and shocks do not decay with time, while the response function of volatility for the other 11 markets, decays with time. Moreover, several diagnostic tools have been implemented. Table 2 in appendix 2 presents the results of five diagnostic tests for the standardized residuals of GARCH (1,1) on a daily basis. The McLeod-Li test indicates that the squared residuals are not correlated; while the BDS test reject the iid hypothesis of the standardized residuals for three Arab markets only (Bahrain, Dubai, and Egypt). 137

149 Table 4-12 GARCH (1,1) Model for Daily Returns Market Obs. α 0 α 1 β 1 α 1 +β 1 ku Sk Q(30) ARCH LM test AbuDhabi Jordan Bahrain Dubai Egypt Kuwait Oman Palestine Saudi USA UK Japan Turkey India Israel Significance levels are in italics. A Chi-square (χ 2 ) tests (α 1 +β 1 ) = 1. Ku is the kurtosis of the residuals. Sk is the skewness of the residuals and Q(30) is the Ljung-Box statistics serial correlation of the lags in the residuals. ARCH LM is a Lagrange multipler (LM) test for autoregressive conditional heteroskedasticity (ARCH) in the residuals. The estimated variance equation is : 2 h a + a ε β h t = 0 1 t t 1 The results change dramatically when we use weekly data, table 4-13 shows the results of GARCH (1,1) for weekly returns. Volatility clustering disappears in 4 out of 9 Arab markets (Abu Dhabi, Dubai, Egypt, and Kuwait) in addition to India and Israel 5, while volatility found to be persistent in 11 markets at the 1 percent level. Within the group of Arab stock markets, the results reveal that 5 of the 9 markets (Abu Dhabi, Dubai, Egypt, Kuwait, and Oman) exhibit persistence of volatility. The results for Jordan, Saudi, and Bahrain presented in table 4-13, are consistent with those obtained from 5 For Egypt, Kuwait, and India, volatility clustering disappears at 1 % level only. 138

150 Haque et al. (2004), since they find that these markets exhibit volatility clustering but not Oman. In addition, they don not find volatility to be permanent in both Saudi and Jordan, which is inconsistent with the results obtained here for these two markets. Table 4-13 GARCH (1,1) Model for Weekly Returns Country Obs. α 0 α 1 β 1 α 1 +β 1 ku Sk Q(30) ARCH LM test AbuDhabi Jordan Bahrain Dubai Egypt Kuwait Oman Palestine Saudi USA UK Japan Turkey India Israel Significance levels are in italics. A Chi-square (χ 2 ) tests (α 1 +β 1 ) = 1. Ku is the kurtosis of the residuals. Sk is the skewness of the residuals and Q(30) is the Ljung-Box statistics serial correlation of the lags in the residuals. ARCH LM is a Lagrange multipler (LM) test for autoregressive conditional heteroskedasticity (ARCH) in the residuals. The estimated variance equation is : h t ε β 2 = a 0 + a 1 t h t 1 - EGARCH model For equities, it is often observed that downward movements in the market are followed by higher volatilities than upward movements of the same magnitude. To account for this phenomenon, Nelson (1991) introduced the EGARCH model which 139

151 allows for asymmetric shocks to volatility and test for the leverage effect. Table 4-14 shows the results of EGARCH for daily returns. The presence of leverage effect can be tested by the hypothesis that γ>0 in table 4-14 and the impact is asymmetric if γ 0. The results indicate that, in addition to the two groups (developed and emerging markets), 4 out of 9 Arab stock markets (Bahrain, Dubai, Kuwait, and Oman) exhibit leverage effect and asymmetric shocks to volatility. Table 4-14 EGARCH (1,1) Model for Daily Returns Market Obs. ω α γ β ku Sk Q(30) ARCH LM test AbuDhabi Jordan Bahrain Dubai Egypt Kuwait Oman Palestine Saudi USA UK Japan Turkey India Israel Significance levels are in italics. A Chi-square (χ 2 ) tests (α 1 +β 1 ) = 1. Ku is the kurtosis of the residuals. Sk is the skewness of the residuals and Q(30) is the Ljung-Box statistics serial correlation of the lags in the residuals. ARCH LM is a Lagrange.EGARCH equation estimated as: 2 2 ε t 1 ε t 1 log( σ t ) = ω + β log( σ t 1) + a + γ σ σ t 1 t 1 140

152 The coefficient of the leverage term was negative and significant at the 5% level; conditional variance is higher in the presence of negative innovations, which indicate that the market becomes more nervous when negative shocks take place. Usually what happens is that, small investors get panic from these negative shocks and sell their stocks in order to avoid higher losses. Moreover, to see this effect clearly, the News Impact Curve has been plotted for each market under examination (see figure 1 appendix 2) Non-linearity in stock returns Efficiency implicitly assumes that investors are rational, where rationality implies risk aversion, unbiased forecasts and instantaneous responses to information. Such rationality leads to price responding linearly to new information. In this contest, failure to take into account the institutional features of emerging markets may lead to statistical illusions regarding efficiency or inefficiency. With reference to evidence in favor of efficiency, this is perhaps the outcome of using linear models for testing efficiency, in markets characterized by inherent non-linearity. If the return generating process is nonlinear and a linear model is used to test for efficiency, then the hypothesis of no predictability may be wrongly accepted, this is because non-linear system such as chaotic ones looks very similar to a random walk. Table 4-15 shows the results of the random walk model including non-linear terms for the observed indices. It appears that with the introduction of non-linear components, α 1, α 2, and α 3 are statistically significant for all markets except Kuwait. This seems to indicate predictability and inefficiency. The results reveal that the return generating process in 8 out of 9 Arab markets is non-linear even after corrected the indices for thin trading (see table 4-16). Moreover, most of the diagnostic tests; especially BDS test, for the residuals indicate that the residual are not following white noise process (see tables 6 and 7 in appendix 2). However, the logistic map is not able to determine the precise nature of any nonlinearity, but rather to ascertain whether non-linearity exists. It is appropriate that nonlinearity generated by dependence in the second moment. To disentangle the nonlinearity generated by changes in volatility from non-linearity arising as a result of other causes, the standardized residuals resulted from GARCH models will be subjected to several 141

153 diagnostic tests to determine whether the specified GARCH model is sufficient to model the nonlinear structure in the data against an unspecified alternative. Table 4-15 Random Walk Models with Non-linearity for Observed Indices Market coefficients std.error t-value P -value Abudhabi Bahrain Dubai Egypt Jordan Kuwait Oman Palestine Saudi R t = a 0 + a 1 R t-1 + a 2 R 2 t-1+ a 3 R 3 t-1+ ε t a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a

154 Table 4-16 Random Walk Models with Non-linearity for Corrected Indices Market coefficient std.error t-value P -value Abudhabi Bahrain Dubai Egypt Jordan Kuwait Oman Palestine Saudi R t adj = a 0 + a 1 R adj t-1 + a 2 R 2adj t-1+ a 3 R 3adj t-1+ ε t a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a Since correct specification of the model implies that the residuals will be uncorrelated and also will have a zero mean and unit variance. Five different tests are considered in this exercise for testing the hypothesis that the residuals are iid. This will 143

155 allow us on one hand to obtain a deeper and more detailed insight into the series properties, by generating useful information from the various tests and on the other hand, to minimize the probability of missing some thing and thus drawing the wrong conclusion. If our battery of tests displays a unanimous consensus in favor of a specific result, we would interpret this consensus as strong corroboration of that output. The five tests that are going to be used are the following: McLeod and Li (1983); Engle (1982), Brock et al. BDS (1996); serial correlation and Jarque-Bera test for normality. All these tests share the principle that once any linear structure is removed from the data, any remaining structure should be due to a non-linear data generating mechanism. The McLeod and Li test looks at the autocorrelation function of the squares of the prewhitened data and test whether corr (e 2 t,e 2 t-k) is non-zero for some k and can be considered as an LM statistic against ARCH effect (see Granger and Terasvirta 1993; Patterson and Ashley 2000). The test suggested by Engle (1982) is an LM test, which should have considerable power against GARCH alternatives. The BDS test is a non parametric test for serial independence as described in section (4-1-6). The corresponding diagnostic tests for random walk and GARCH models for each daily observed index are presented in appendix 2. Table 1 in appendix 2 shows the diagnostic tests for the ordinary residuals of the RW model, clear evidence emerges across the spectrum of tests that the residuals of the RW are not iid. Almost all P-values are 0, suggesting that some kind of hidden structure exists in the data. While for Dubai equity market, only the BDS test rejects the iid hypothesis. The failure of the RW model to explain the behavior of the series and considerations of the constant term that are statistically different from zero, for some models, casts doubts on the validity of the weak-form efficiency. The evidence against EMH is clear in all of the indices. Table 2 in appendix 2 presents the results of diagnostic tests for the standardized residuals obtained from GARCH models. Evidence emerges to support the hypothesis that the standardized residuals are iid for all markets except Bahrain, Dubai, and Egypt, since the BDS test for these three markets, reject the iid hypothesis. Table 3 in appendix 2 presents the results for the standardized residuals obtained from EGARCH model for observed daily indices. The results indicate that, we are not able to reject the iid hypothesis for all markets. Most of the P-values presented in table 3, 144

156 are exceed the 5% benchmark. Moreover, the GARCH models produced lower SC s (Schwartz criterion) and as a result, are preferred to the RW in this respect. While for Bahrain, Abu Dhabi, and Saudi, the EGARCH models produced the lowest SC s values, compared to the lowest SC s values which have been produced by GARCH models for the other markets. Additionally, evidence emerges to support the hypothesis that the standardized residuals of the GARCH models are iid. Most of the P-values exceed the 5% level. Therefore, we could accept the randomness hypothesis Calendar effects As mentioned previously, the existence of seasonality or calendar effects in stock markets, contradicts the efficient market hypothesis, at least in its weak form, because the predictable movements in asset prices provide investors with opportunities to generate abnormal returns. In emerging markets, it is possible that the dissemination of information is restricted due to the possible manipulation of financial information by market participants, and a lack of strict disclosure requirements imposed by the stock market regulatory agencies. In this section, we will examine three calendar effects, dayof-the-week, month-of-the-year, and the Halloween effects for each of the Arab stock markets under examination. - Day-of-the-week effect Table 4-17 shows the OLS results for the day-of-the-week effect. Because Arab stock markets have different trading days during the week, the OLS equation will have five dummy variables for markets which have five trading days, and six dummy variables for those with six trading days. Moreover, our purpose here is to test for the effect of the first trading day of the week for each market, which will be Saturday or Sunday, since these days are trading days in Arab stock markets (weekend is Friday) and it is equivalent to test for Monday effect in other international stock markets. 6 The results in table 4-17 indicate that, the estimated coefficients for the first trading day of the week are positive and statistically significant (at 5% level) in three markets Abu Dhabi, Jordan, and Saudi Arabia. While these coefficients found to be 6 Palestine was not included in this test, since trading days are not consistent during the week, resulting irregularity in trading days. 145

157 positive but not significant in the rest of the markets. Moreover, the coefficients for other days found to be positive and significant for most of the markets, another important note is that for Bahrain and Saudi, the next trading day found to be negative and statistically significant (Monday and Sunday effects). It seems that in these two markets, bad news is announced at week ends, in order for the shock to be more easily absorbed, but that the information is not instantly reflected in prices, investors in these markets hesitant and act with a delay of one day. Furthermore, the estimated coefficients for all trading days in both Jordan and Abu Dhabi found to be positive and statistically significant (at 5% level). In general, the results are inconsistent with the results reported in the finance literature for a large number of countries, where significantly lower or negative Monday returns are reported (the traditional Monday effect). In addition, the results reject the trading time hypothesis (since the returns on the first day is different from that of the other days of the week), as well as the calendar time hypothesis (since the returns on the first trading day is not three times that of the other days). Note also that, the other coefficients in several markets are significantly positive. The results for Egypt are inconsistent with those obtained by Aly et al. (2004), since they find that all estimated coefficients to be positive but statistically not significant. However, a Chow test indicates that the estimated coefficients reported in table 4-17, are structurally stable over the entire sample period for three markets only (Abu Dhabi, Bahrain, and Kuwait), while the hypothesis that all parameters equal zero has been soundly rejected for all markets (see table 4-18). In order to further investigate the presence of a positive first trading day seasonality in Arab stock markets, one can test this formally by examining whether there exists a simultaneous day-of-the-week effect in mean returns and in the standard deviation of these returns. A formal test of the existence of day-of-the-week calendar effect is given by the ANOVA or Kruskal-Wallis statistics. While a formal test for daily variation in the second moment is given by the Levene test. Table 4-19 shows the results of the day-of-the-week effect in the first two moments. As can be seen in table 146

158 Table 4-17 OLS Results for Day-of-the-Week Effect Market Variable Coefficient Std. Error t-statistic Prob. AbuDhabi 1/7/ /12/2003 Saturday Sunday Monday Tusday Jordan Wedensday /1/ /3/2005 Sunday Monday Tusday Wedensday Thursday Bahrain 2/1/1991-3/6/2004 Sunday Monday Tusday Wedensday Thursday Dubai 26/3/ /12/2003 Saturday Sunday Monday Tusday Wedensday Thursday Egypt 1/1/ /12/2004 Sunday Monday Tusday Wedensday Kuwait 17/6/ /11/2005 Saturday Sunday Monday Tusday Wedensday Oman 25/6/ /10/2004 Sunday Monday Tusday Wedensday Thursday Saudi Arabia 26/1/ /3/2005 Saturday Sunday Monday Tusday Wedensday Thursday The estimated equation is: R.where R t is the daily return t = β 1 D1 + β 2D β 6D6 + ε t which defined as ln(p t /p t-1 );D 1 through D 6 are dummy variables such that if t is a Saturday, then D 1 =1 and D1=0 for all other days, if t is a Sunday D 2 =1and D 2 =0 for all other days, and so forth; ε t is a random term and β 1 -β 6 are coefficients to be estimated using ordinary least squares according to the trading days for each market. 147

159 Table 4-18 Chow Test for Structural Stability Market AbuDhabi Jordan Bahrain Dubai Egypt Kuwait Oman Saudi Break point 1-Jul Jul Sep-97 2-Mar-02 2-Jul-01 4-Nov Aug-02 1-Sep-99 F -test P -value Chow test is implemented to test that the estimated coefficients are structurally stable over the entire sample period. Wald Coefficient Restrections Test Market AbuDhabi Jordan Bahrain Dubai Egypt Kuwait Oman Saudi Chi-square Probability Wald test tests the null hypothesis that β1 = β 2 =... = β 6 = 0 Table 4-19 Day-of-the-Week Effect in the First Two Moments Market First trading day of the week AbuDhabi Saturday Jordan Sunday Bahrain Sunday Mean Stand. Dev. Mean Stand. Dev. Mean Stand. Dev. Returns on 1st trading day Returns during Rest of the Week Difference of Means Test 5.919** 6.147** difference of Variance Test * * 17.3* Market First trading day of the week Dubai Saturday Egypt Sunday Kuwait Saturday Mean Stand. Dev. Mean Stand. Dev. Mean Stand. Dev. Returns on 1st trading day Returns during Rest of the Week Difference of Means Test * difference of Variance Test * 625.6* * Market First trading day of the week Oman Sunday Saudi Saturday Mean Stand. Dev. Mean Stand. Dev. Returns on 1st trading day Returns during Rest of the Week Difference of Means Test 3.619*** 4.452** difference of Variance Test * * *,**,*** indicate significant at 1%, 5%, 10% levels, respectively. Difference of means test of the null hypothesis that the mean return of the first trading day of the week, is equal to the mean return during the rest of the week. This test is based on a single-factor, between subjects, analysis of variance (ANOVA) Difference of variance test of the null hypothesis that the variance of the first trading day return is equal to the variance return of the rest of the week, depending on the Levene test 148

160 4-19, the difference-of-means test is statistically significant for all markets except Bahrain, Dubai, and Egypt. Indicating that, the first trading day returns are significantly positive and different from the returns during the rest of the week. Moreover, the standard deviation of the first trading day is lower than the standard deviation during the rest of the week, and a difference-of-variance test shows that the difference is statistically significant for all markets, which indicate that first trading day returns are significantly less volatile than returns during the rest of the week. -Month-of-the-year effect (January effect) Table 4-20 gives the results for January effect and those months, for which their parameters found to be statistically significant. Despite the results that Bahrain, Dubai, Oman, and Saudi Arabia do not exhibit January effect, all markets show signs of monthly effect other than January; since several months parameters found to be positive and statistically significant. The results for Jordan are incompatible with those of Maghayereh (2003) since he does not find monthly effect in the Jordanian stock market, whilst the results for Kuwait are constant with those of Al-saad and Moosa (2005) and Al-loughani (2003). Table 4-20 OLS Results for Month-of-the-Year Effect (January Effect) Market Variable Coefficient Std. Error t-statistic Prob. Abu Dhabi 1/7/ /12/2003 JANUARY FEBROUARY MARCH ABRIL MAY JUNE JULY AUGUST SEPTEMBER OCTOBER NOVEMBER DECEMBER Jordan 1/1/ /3/2005 JANUARY ABRIL MAY JUNE JULY AUGUST SEPTEMBER OCTOBER NOVEMBER

161 continue table 4-20 Market Variable Coefficient Std. Error t-statistic Prob. Bahrain 2/1/1991-3/6/2004 JANUARY MAY AUGUST Dubai 26/3/ /12/2003 JANUARY JULY AUGUST Egypt 1/1/ /12/2004 JANUARY ABRIL JULY AUGUST SEPTEMBER OCTOBER NOVEMBER DECEMBER Kuwait 17/6/ /11/2005 JANUARY MARCH ABRIL MAY JUNE SEPTEMBER Oman 1/2/ /10/2004 JANUARY FEBROUARY MARCH ABRIL MAY JUNE JULY AUGUST SEPTEMBER OCTOBER NOVEMBER DECEMBER Palestine 8/7/ /2/2005 JANUARY FEBROUARY ABRIL MAY JULY SEPTEMBER OCTOBER Saudi Arabia 26/1/ /3/2005 JANUARY FEBROUARY MARCH ABRIL MAY JULY SEPTEMBER NOVEMBER The estimated equation is: R. Where R t is the daily index t = α 1 D1 + α 2D α12d12 + ε t return which defined as ln(p t /p t-1 ), α 1 through α 12 are coefficients to be estimated using ordinary least squares.d 1 -D 12 are dummy variables such that if t is January, then D 1 =1 and D 1 =0 for all other monthes, if t 150

162 Furthermore, table 4-21 shows the result of Chow test which indicates that the estimated coefficients are structurally unstable for three markets (Egypt, Palestine, and Saudi), whereas the hypothesis that all parameters are insignificantly different from zero, has been rejected for all markets. Table 4-21 Chow Test for Structural Stability Market AbuDhabi Jordan Bahrain Dubai Egypt Kuwait Oman Palestine Saudi Break point 1-Jul Jul Sep-97 2-Mar-02 2-Jul-01 4-Nov Aug Oct-01 1-Sep-99 F -test P -value Chow test is implemented to test that the estimated coefficients are structurally stable over the entire sample period. Wald Coefficient Restrections Test Market AbuDhabi Jordan Bahrain Dubai Egypt Kuwait Oman Palestine Saudi Chi-square Probability Wald test tests the null hypothesis that β 1 = β 2 =... = β 12 = 0 Moreover, table 4-22 indicates that the variance of returns are not equal, between January and the rest of the year, while the mean returns in January and the rest of the year found to be equal only in three markets (Bahrain, Dubai, and Egypt). The majority of markets demonstrate some type of monthly effect, which could be confusing in that, it cannot be explained under the umbrella of the existing explanations in the literature. For instance, window press such that, investors sell in December those stocks that did not doing well, in order to show reduced profit and pay less tax. This would results in lower returns in December, whilst in January returns should rise due to reinvestment in new stocks, but in our case, all returns in December are positive or not significant. Another explanation could be summer holiday, in which we expect returns in July and August to be negative, the case which exists only in Bahrain (negative August returns). However, in the case of Arab stock markets; especially GCC stock markets; they witnessed a considerable improvements in their activity and liquidity (see figures 2-2 and 2-4), as a result of the huge raise in oil prices in the last two years, which produces a surplus in liquidity in these countries, leading to increase the trading activity in their financial markets. 151

163 Table 4-22 Month-of-the-Year Effect in the First Two Moments Market AbuDhabi Jordan Bahrain Mean St. Dev. Mean St. Dev. Mean St. Dev. Returns on January Returns during Rest of the year Difference of Means Test 6.868** 7.621** difference of Variance Test * * 19.91* Market Dubai Egypt Kuwait Mean St. Dev. Mean St. Dev. Mean St. Dev. Returns on January Returns during Rest of the year Difference of Means Test * difference of Variance Test 218.4* * * Market Oman Palestine Saudi Mean St. Dev. Mean St. Dev. Mean St. Dev. Returns on January Returns during Rest of the year Difference of Means Test 3.49*** 4.081** 9.433* difference of Variance Test * * * *,**,*** indicate significant at 1%, 5%, 10% levels respectively. Difference of means test of the null hypothesis that the mean return of January, is equal to the mean return during the rest of the months. This test is based on a single-factor, between subjects, analysis of variance (ANOVA) Difference of variance test of the null hypothesis that the variance of January return is equal to the variance return of the rest months depending on the Levene test. - The Halloween effect Figure 4-2 presents the average returns in the period May-October and the period November-April for each market. As can be seen in figure 4-2, the differences in returns among the two half-year periods are generally large and economically significant for 5 out of 9 markets. Figure 4-2 Average Returns Among the Two Half-Year Periods 152

164 Table 4-23 shows the Halloween indicator in Arab stock markets. As mentioned previously, a positive and significant α 1 parameter is evidence of a Halloween effect. Since α 1 denotes the average returns in the period November-April in excess of the average return during the other six months of the year. Table 4-23 indicates that, all markets (except Dubai and Saudi) exhibit highly statistically significant Sell in May effect at the 1% level. Table 4-23 The Halloween Indicator in Arab Stock Markets R µ + α + ε t = 1S t N. of Countries observation Mean P-Value α 1 P-value AbuDhabi Bahrain Dubai Egypt Jordan Kuwait Oman Palestine Saudi R t represents monthly continuously compounded returns for the price indices. N, the number of daily observations. The constant term µ represents the daily mean returns over the May- October periods.the daily mean return over the November-April periods is represented by µ+α 1. t Following Fama s arguments (Fama; 1998) that most long-term returns anomalies tend to disappear with reasonable changes to technique, since Sell in May hypothesis suggests that; average returns are higher during the period November-April than during the period May-October. One might argues that, since the January effect generates high positive returns in many stock markets, the Sell in May effect is simple a January effect in disguise. To test for this hypothesis, we considered an additional regression and give Sell in May Dummy the value 1 in the period November to April, except January. While for January, we add an additional dummy. Table 4-24 presents the results of the Halloween indicator with adjustment for January effect, the results indicate that all access returns in January are entirely due to January effect (α 2 ) and not caused by Sell in May effect. Note that, the Halloween effect which presented by α 1 still the same, highly 153

165 statistically significant without any noticeable reduction in α 1 s values except for Egypt. Indicating that despite the addition of January dummy, the Sell in May effect still exists in 7 out of 9 Arab stock markets. Table 4-24 The Halloween Indicator in Arab Stock Markets with January Effect Adjustment Rt = µ + α1 St + α 2J t + ε t Countries Mean P-Value α 1 P-value α 2 P-value AbuDhabi Bahrain Dubai Egypt Jordan Kuwait Oman Palestine Saudi R t represents monthly continuously compounded returns for the price indices. N, the number of daily observations. The constant term µ represents the daily mean returns over the May-October periods.the daily mean return over the November-April periods is represented by µ+α 1. The impact of January returns represented by α Summary We have tried to answer the question whether Arab stock markets are efficient in the weak-form sense. The empirical results obtained, enable us to provide strong evidence against the random walk hypothesis for Arab stock markets. The results obtained from regression analysis, variance ratio, BDS, runs test, and serial correlation tests, reject the randomness and independence of the returns generating process, even after indices have been corrected for infrequent trading. These results are consistent with the existing literature for emerging markets, since many evidences of predictability in emerging markets have been found and rejected the hypothesis that lagged price information cannot predict future prices (Bekaert 1995; Harvey 1995b, 1995c; Claessense et al. 1995; Buckberg 1995; Haque et al. 2001, 2004; and Bailey at al. 1990). Moreover, the results indicate that, prices responding non-linearly to new information, while volatility clustering phenomenon still seems to characterized markets returns. The GARCH (1,1) results for daily data indicate that, all markets exhibit volatility clustering with one exception for Dubai. Furthermore, volatility seems to be persistent in three 154

166 markets (Egypt, Kuwait, and Palestine) with a slow rate of decay, Oman displays an increasing response function of volatility; shocks do not decay with time. Additionally, four Arab markets (Bahrain, Dubai, Kuwait, and Oman) show signs of leverage effect and asymmetric shocks to volatility. The results also indicate that, the GARCH models explain quite satisfactory the dependencies of the first and second moments; that are presented in the stock return series, while the second moment found to be quite enough to explain the non-linearity structure that has been found in the time series. The next task of this chapter was to investigate the existence of calendar effects in Arab stock markets. The results indicate that three calendar effects found to be in Arab markets, day-of-the-week, month-of-theyear effects, and the Halloween indicator. However, the style of the first two anomalies is not consistent with the existing literature. For instance; for most of the Arab markets; the first trading day of the week found to be positive and significantly different from the rest of the week s returns, while several months of the year found to be significantly positive. None of the existing explanations in the literature (tax loss, trading time, calendar time hypothesis) reveal to be appropriate to explain these anomalies. One explanation might be; according to the surplus liquidity between investors (especially GCC investors), as a result of the sharp rise in oil prices and according to the lack of other investment opportunities, site these markets as an attractive target for these investments. This leads to huge improvements in most markets indicators during the last three years such as market size and liquidity indicators. In sum, the results obtained from this chapter enable us to declare that, Arab stock markets under examination here are not efficient in the weak-form sense. 155

167 Methodology Are Arab Stock markets Integrated among themselves and With Other International Stock Markets? If Yes, How Do Shocks Generated By International Stock Markets Especially UK, US, and Japan Affect Arab Stock Markets? Unit root test ADF, PP, and KPSS. Multivariate cointegration, Johansen cointegration test. Causality and vector error correction models (VEC). Structural vector autoregressive (SVAR). Vector auto regressive (VAR). Granger causality test. Correlation coefficients. Findings Can Arab Stock Markets Offer, for Both Regional and International Investors Unique Risk and Returns Characteristics to Diversify International and Regional Portfolios? Arab stock markets are segmented from international markets, even in the short term horizon. The segmentation also exists between non-oil countries. One cointegrating relation was found among Arab stock markets. Weak linkages between Arab markets in the short run. GCC stock markets found to be integrated with one cointegration relation, with weak short-term interrelation. Arab stock markets can offer both international and regional portfolio investors with diversification potentials. 156

168 Methodology What Is the Effect of Oil Prices on the Performance of GCC Stock Market?, And Whether These Markets Have Predictive Power on Oil Prices or Vice Versa? GARCH (1,1) model with oil prices as an additional regressor in variance equation. Multivariate cointegration analysis between GCC markets and oil prices. Vector error correction model among oil prices and GCC markets. Vector autoregressive (VAR) analysis between oil and GCC markets (under the form of event studies). Findings There is cointegrating relation between oil and GCC markets. Oil prices have significant role on GCC returns volatility. After the raise in oil prices, four of GCC markets can predict oil prices while only two markets can be predicted by oil prices. 157

169 5- Financial integration and diversification benefits among Arab and international stock markets Over the last 20 years, financial markets have become highly integrated, mainly due to reductions in the cost of information, improvements in trading systems technology and the relaxation of legal restrictions on international capital flows. The changes have accelerated the interaction among financial markets and the enlargements of capital mobility. Moreover, gains from international portfolio diversification are related inversely to the correlation of equity returns, according to modern portfolio theory. In line with this theory, investors have become highly active, investing in foreign equity markets as a risk diversification strategy. Numerous studies have demonstrated the advantage of international diversification related to low correlation between various equity markets, such as Eun and Resick (1984), Wheatly (1988), Meric and Meric (1989), Baily and Stulz (1990), Divecha et al. (1992), Michaud et al. (1996), Meric et al. (2001), and Bulter and Joaquin (2002). As developed in Gilmore and McManus (2002), the low correlations could be explained by different types of barriers and regulations between the markets under consideration. However, recent studies outlined in Gilmore and McManus (2002) reveal a significant increase in correlations between equity markets, especially during and after the 1987 international equity markets crash. This tendency for the global markets to become more integrated is a result of the increasing tendency toward liberalization and deregulation in the money and capital markets, both in developed and developing countries as well as on a bilateral and multilateral basis, commencing from, for example, trade liberalization and multilateral trade initiatives. Such liberalization is important to introduce structural reforms, to promote economic efficiency, to estimate trade and investment, and to create a necessary climate for promoting sustainable economic growth with a commitment to market-based reforms. As a sequence, increases in correlations between markets would imply a decrease in the benefits from international diversification in line with portfolio theory. Furthermore, long-run linkages between stock markets have important regional and global implications at the macro-level, as a domestic capital market cannot be insulated adequately from external shocks, thus the scope for independent monetary 158

170 policy may become limited. It is argued in Errunza et al. (1999) that the use of return correlations at the market index level to infer gains from international diversification, involving foreign-traded assets overstates the potential benefits. The gains must be measured beyond those attainable through home-made diversification by mimicking returns on foreign market indices with domestically traded securities. In addition, Kasa (1992) argued that benefits from international diversification evaluated by low correlations, can be overstated if the investor has a long-term investment horizon and the markets are trading together. The implication of this is that, any benefits that arise from diversification will be eradicated in the long run and, therefore, investors with long-run horizons may not actually benefit from international portfolio diversification. As a sequence, recent studies have used cointegration techniques to analyze long-run linkages and co-movements, especially between US and various emerging markets. The results concerning long-term diversification benefits for US investors are somewhat mixed. Long-term linkages between US and various European stock markets are found in Kasa (1992) and Arshanapalli and Doukas (1993), but results in Bayers and Peel (1993), Kanas (1998), and Maneschiold (2004) suggest that there are no such linkages. Mixed results also found between the USA and the Pacific area, as discussed in Gultekin et al. (1989), Harvey (1991), Cambell and Hamao (1992), and Sewell et al. (1996), as well as for a group of Asian countries, as discussed in Neih and Chang (2003), Gilmore and McManus (2002). While the integration between some Arabian markets and US discussed in Maneschiold (2005), who finds that Egypt can offer diversification benefits for US investors. Darrat and Hakim (2000) find that stock markets in the Middle East are segmented from the global markets. While Neaime (2002) finds that GCC stock markets, still offer diversification benefits for international investors. However, the results between US and international markets reveal, in general, a higher degree of independence with emerging markets than developed markets. Given the increased correlations in recent periods between various international equity markets and the general results from previous studies, investors are searching for markets that are more promising for diversification benefit, Gilmore and McManus (2002) find evidence of diversification benefits between US markets and the newly re-opened markets in the 159

171 Czech Republic, Hungary and Poland, while Maneschiold (2004) finds evidence of this between the USA and the Baltic states. The purpose of this chapter is to analyze possible diversification benefits, which Arab stock markets may offer for both regional and international investors. The analysis will be carried out on different levels. Firstly, the diversification potentials for international investors will be analyzed. The US stock market (S&P 500) will be used to represent the world markets, in view of the growing evidence that assigns considerable weight to the US market in global capital markets. Second, the integration among Arab stock markets them selves will be investigated. And finally, it is important to take into account the important features that affect Arab stock markets, such as oil prices, so the dynamic relationships between GCC stock markets and oil prices will be analyzed. 5-1 International integration of Arab stock markets In order to investigate the possible diversification benefits for international investors in the Middle East region, the analysis will relay on the Johansen cointegration procedures, as well as Granger causality and correlation tests, to reveal short and longrun relationship between Arab and international markets, and the possible diversification benefits for US investors on a multivariate basis. Moreover, to investigate the dynamic relationship in the short-run between Arab and international stock markets, the structural vector auto regressive (SVAR) will be used to trace the effect of shocks, generated by international stock market, on Arab markets Unit root test A prerequisite for cointegration is that non-stationary series are integrated of the same order. Therefore, the first step is to determine the order of integration for each variable. Three tests will be employed in this investigation: the augmented Dickey-Fuller, the Phillips-Perron, and the Kwaitkowski-Phillips-Schmidt-Shin (KPSS) tests (Dickey and Fuller 1979; Phillips and Perron 1988; Kwaitkowski et al. 1992). The basic features of unit root tests can be presented as follows, consider a simple AR(1) process: 160

172 y t ρ y + δ x + ε, = (5-1) t 1 t t where x t are optional exogenous regressors which may consist of constant, or a constant and trend, ρ and δ are parameters to be estimated, and the ε t are assumed to be white noise. If ρ 1, y is a non stationary series and the variance of y increases with time and approaches infinity. If ρ <1, y is a (trend-) stationary series. Thus the hypothesis of (trend-) stationary can be evaluated by testing whether the absolute value of ρ is strictly less than one. In general, one can test the null hypothesis H 0 : ρ=1against the one-side alternative H 1 : ρ <1. In some cases, the null is tested against a point alternative. In contrast, the KPSS Lagrange Multiplier test evaluates the null of H 0 : ρ<1 against the alternative H 1 : ρ=1. - The augmented Dickey-Fuller (ADF) test The standard Dickey-Fuller test is carried out by estimating (5-1) after subtracting y t-1 from both sides of the equation: y t = α y + δ x + ε 1 (5-2) t t t where α=ρ-1. The null and alternative hypothesis may be written as H 0 : α=0, H 1 : α<0 and evaluated using the conventional t-ratio for α: ta = aˆ /( se( aˆ)) where âis the estimated α, and se(aˆ ) is the coefficient standard error. Dickey and Fuller (1979) show that under the null hypothesis of a unit root, the statistic does not follow the conventional Student s t-distribution, and they derive asymptotic results and simulate critical values for various test and sample sizes. More recently, MacKinnon (1991, 1996) implements a much larger set of simulations than those tabulated by Dickey and Fuller. In addition, MacKinnon estimates response 161

173 surfaces for the simulation results, permitting the calculation of Dickey-Fuller critical values and P-values for arbitrary sample size. The simple Dickey-Fuller unit root test described above is valid only if the series is an AR(1) process. If the series is correlated at higher order lags, the assumption of white noise disturbances ε t is violated. The Augmented Dickey-Fuller (ADF) test constructs a parametric correction for higher order correlation by assuming that the y series follows an AR(p) process and adding p lagged difference terms of the dependent variable y to the right-hand side of the test regression: y = ay + t t 1 + δ xt + β1 yt 1 + β 2 yt β p yt p ut (5-3) this augmented specification is then used to test the null hypothesis that H 0 : α=0 against H 1 : α<0, using the t-ratio. An important result obtained by Fuller is that the asymptotic distribution of the t-ratio for α is independent of the number of lagged first differences included in the ADF regression. Moreover, while the assumption that y follows an autoregressive (AR) process may seem restrictive, Said and Dickey (1984) demonstrate that the ADF test is asymptotically valid in the presence of a moving average (MA) component, provided that sufficient lagged differences terms are included in the test regression. Moreover, there are two practical issues in performing an ADF test. First, one must choose whether to include exogenous variables in the test regression. You have the choice of including a constant, a constant and a linear time trend, or neither, in the test regression. One approach would be to run the test with both a constant and a linear trend since the other two cases are just special cases of this more general specification. However, including irrelevant regressors in the regression will reduce the power of the test to reject the null of a unit root. Second, you will have to specify the number of lagged difference terms, to be added to the regression (zero yields the standard DF test; integers greater than zero correspond to ADF tests). 162

174 - The Phillips-Perron (PP) test The PP method proposes as an alternative (non-parametric) method of controlling the serial correlation with testing for a unit root. The PP method estimates the nonaugmented Dickey-Fuller test, and modify the t-ratio of the α coefficient, so that, serial correlation does not affect the asymptotic distribution of the test statistic. The PP test is based on the statistic 1/ 2 ( f )( se( aˆ )) ~ γ 0 T 0 γ 0 ta = ta 1/ 2 f (5-4) 0 2 f 0 s where â is the estimate; t a the t-ratio of α, se( â) is coefficient standard error; s is the standard error of the test regression. In addition, γ 0 is a consistent estimate of the error variance calculated as (T-k) s 2 /T, where k is the number of regressors. The remaining term, ƒ 0, is an estimator of the residual spectrum at frequent zero. - The Kwiatkowski, Phillips, Schmidt, and Shin (KPSS) test The Kwaitkowski et al. (1992) KPSS test, differs from the other unit root tests in that, the series y t is assumed to be (trend-) stationary under the null. The KPSS statistic is based on the residuals from the OLS regression of y t on the exogenous variables X t : the LM statistic is be defined as: y = x δ + u (5-5) t t t 2 ( t) 2 / ( T f ) LM = s (5-6) t 0 where f 0, is an estimator of the residual spectrum at frequency zero and where s(t) is a cumulative residual function: s t u t r= 1 ( t) = Multivariate cointegration The finding that many macro time series may contain a unit root has spurred the development of the theory of non-stationary time series analysis. Engle and Granger 163

175 (1987) pointed out that a linear combination of two or more non-stationary series may be stationary. If such a stationary linear combination exists, the non-stationary time series are said to be cointegrated. The stationary linear combination is called the cointegrating equation and may be interpreted as a long-run equilibrium relationship among the variables. The purpose of the cointegration test is to determine whether a group of nonstationary series is cointegrated or not. The presence of a cointegrating relation forms the basis of the VEC specification. To check whether the series are cointegrated, specifically, having established the presence of a unit root in the first-difference of each variable, we need to test whether the series in each market has different unit root (non-cointegrated), or share the same unit root (cointegrated). Cointegrated variables, if disturbed, will not drift apart from each other and thus possess a long-run equilibrium relationship. The existence of cointegration between two series would imply that the two series would never drift too far apart. A non-stationary variable, by definition, tends to wander extensively over time, but a pair of non-stationary variables may have the property that a particular linear combination would keep them together, that is, they do not drift too far apart. Under this scenario, the two variables are said to be cointegrated, or possess a longrun (equilibrium) relationship. Cointegration can be tested on bivariate base using two stage method of Engle and Granger (1987), while a multivariate cointegration test can be carried out using Johansen (1988) approach based on the autoregressive representation. If there are two variables, x t and y t, which are both non-stationary in levels but stationary in first difference, then x t and y t are integrated of order one, I(1), and their combination having the form z t = x ay is also I(1). However, if z t is integrated of t t order zero, I(0), the linear combination of x t and y t is stationary and the two variables are said to be cointegrated. If two variables are cointegrated, there is an underlying long-run relationship between them. In the short-run the series may drift apart, but if they are cointegrated, they will move toward long-run equilibrium through an error-correction mechanism. If x t and y t are integrated of the same order, the Engle-Granger method then estimates the long-run equilibrium relationship as: 164

176 y β + x + e (5-7) t = 0 β1 t t using ordinary least squares (OLS). The residual series e t is then tested for stationary: e = α + ε t 1 e t 1 t (5-8) rejection the null hypothesis α 1 = 0 implies that the residual series is stationary and that the two series are cointegrated. Engle and Granger provide statistics to test the hypothesis α 1 = 0. When more than two variables are involved, the appropriate tables are of those of Engle and Yoo (1987). If the variables are found to be cointegrated, an error-correction model can then be estimated using the residuals from the equilibrium regression: y t 1 + α yet 1 + α11 yt i + α12 xt i ε yt (5-9) = α + x t 2 + α xet 1 + α 21 yt i + α 22 xt i ε xt (5-10) = α + The Engle-Granger procedure has several problems. It is a two-step procedure, so any error introduced in estimating the error term comes into the error-correction model. Also, it requires that one variable be placed on the left-hand side of the equation, with the other variables as regressors in the cointegrating equation. The results of one regression may indicate that the variables are cointegrated, while the other regression suggests no cointegration. The Johansen (1988) approach circumvents the use of two-step estimators and estimates as well as tests for the presence of multiple cointegrating vectors. This method relies on the relationship between the rank of a matrix and its characteristic roots, or eigenvalues. Let x t be a vector on n time series variables, each of which is integrated of order (1) and assume that x t can be modeled by a vector autoregressive (VAR): y t = A1 yt Ap yt p + β xt + ε t (5-11) 165

177 where y t is a k-vector of non-stationary I(1) variables; x t is a d-vector of deterministic variables; and ε t is a vector of innovations, we can write this VAR as y t = y p 1 t 1 + Γi yt i + β xt + ε t (5-12) i= 1 where p = i= 1 A i p I, Γ = A (5-13) i j= i+ 1 j Granger s representation theorem asserts that, if the coefficient matrix has reduced rank r<k, then there exist k*r matrices a and β each with rank r such that Π = α β and β yt is I(o). r is the number of cointegrating relations (the cointegrating rank) and each column of β is the cointegrating vector. The elements of α are known as the adjustment parameters in the VEC model. Johansen s method is to estimate the matrix from an unrestricted VAR, and test whether we can reject the restrictions implied by the reduced rank of. Three cases are possible, first, if Π is of full rank, all elements of y are stationary and none of the series has a unit root. Second, if the rank of Π=0, there are no combinations which are stationary and there are no cointegrating vectors. Third, if the rank of Π is r such that 0<r<k, then the y variables are cointegrated and there exist r cointegrating vectors. The number of distinct cointegrating vectors can be obtained by determining the significance of the characteristics roots of Π. To identify the number of characteristic roots that are not different from unity, we can use two statistics, the trace test and the maximum eigenvalue test: λ ( r) T ln(1 λ ) (5-14) trace = i λ r, r 1) = T ln(1 λ ) (5-15) max ( + r

178 where λ equals the estimated values of the characteristic roots (eigenvalues) obtained from the estimated Π matrix, r is the number of cointegrating vectors, and T equals the number of usable observations. The trace test evaluates the null hypothesis that the number of distinct cointegrating vectors is less than or equal to r against a general alternative. The maximum eigenvalue test examines the number of cointegrating vectors versus that number plus one. If the variables in y t are not cointegrated, the rank Π is zero and all characteristics roots are zero. Since Ln (1) = 0, each of the expression Ln (1-λ i ) will equal zero in that case. Critical values for the test are provided by Johansen and Juselius (1990) and by Osterwald-Lenum (1992) Structural VAR (SVAR) When Sims (1980) introduced vector autoregression (VAR) into economics, the main thrust was that VAR modeling avoids incredible identifying assumptions made by traditional large-scale macroeconomic models. Subsequently, the great bulk of structural VAR work has focused on contemporaneous relationships between variables or between residuals in a system of equations. Sims and Zha (1999) show how to make Bayesian inference under a flat prior in both reduced-form VARs and identified VARs. In that paper, they consider various types of identifying restrictions only on contemporaneous coefficients. There are instances, however, in which over-identification in VAR relates to lag structure as certain lags do not enter certain equations. In many empirical applications, such restrictions are not unreasonable; on the contrary, restrictions on the lag structure are necessary precisely on the ground of economic reasoning. These situations frequently stem from some block exogeneity restrictions such as the crucial small-open-economy feature in international economics from some beliefs that certain lags do not appear in certain equations (e.g., Zellner and Plam, 1974; Zellner, 1985; Leeper and Gordon, 1992; Sims and Zha, 1995; Bernank et al., 1997). Failing to impose these restrictions because they may complicate statistical inference not only is economically unappealing, but also may result in misleading conclusions. 167

179 In order to introduce the basic elements of VAR analysis, suppose that we can represent a set of n economic variables using a vector (a column vector) y t of stochastic processes, jointly covariance stationary without any deterministic part and possessing a finite order (p) autoregressive representation A A( L) = ε (5-16) y t t p ( L) = I A1 L... A p L (5-17) the roots of the equation det[a(l)] are outside the unit circle in the complex domain and ε t has an independent multivariate normal distribution with zero mean ε t IMN(0,Σ) E(ε t ) = 0 E ε ε ) = Σ det( Σ ) 0 ( t t [ 0] E( ε ε ) = s t t s in other words, ε t is a normal distributed vector white noise (VWN). The y t process has a dual Vector Moving Average representation (Wold representation) Y t = C(L) ε t C(L) = A(L) -1 C(L) = I + C 1 L + C 2 L 2 + Where C(L) is a matrix polynomial which can be of infinite order and for which it has been assumed that the multivariate invertibility conditions hold, i.e. det[c(l)] = 0 has all the roots outside the unit circle, so C(L) -1 = A(L) Suppose that there are T+p observations for each variable represented in the y t vector; we are thus able to study the system A(L)y t = ε t t=1, T (5-18) 168

180 This system can be conceived as a particular reduced form (in which all variables can be seen as endogenous). A VAR model has to be considered as a reduced form model where no explanations of the instantaneous relationships among variables are provided. These instantaneous relationships are naturally hidden in the correlation structure of Σ matrix, and left completely un-interpreted. This becomes evident when the model is put into its equivalent Vector Moving Average (VMA) representation, where the interpretability of the coefficients becomes problematic, given the contemporaneous correlation structure of the error terms. Sims (1980) original proposal consisted in moving from a nonorthogonal VMA to an orthogonalized VMA representation via Choleski factorization of the Σ matrix. This amounts to starting from the reduced form VAR representation A(L)y t = ε t, ε t VWN(0,Σ) where ε t is a normal distributed vector white noise (VWN), and to pre-multiply the system by the inverse Choleski factor of Σ * A ( L) = A * (L)y t = e t, e t VWN (0,I n ) p i= 0 A A = p A = p A * * 1 * 1 i, 0, i i, pp = Σ where p is the Choleski factor of Σ, and clearly * A 0 is lower triangular with unit diagonal elements. This amounts to modeling contemporaneous relationships among the endogenous variables in a triangular recursive form. The resulting orthogonal VMA representation is y t = C pe = Φ e, =, Φ p i= 0 i t i i= 0 i t i Φ i C i 0 = notice that, since Φ 0 = p, the orthogonal VMA representation shocks e t have instantaneous effects on the elements of y t according to the triangular scheme given by the Choleski factor p. 169

181 Moreover, it is true that given the matrix Σ, the Choleski factor p is uniquely determined. Nevertheless, if the elements of y t were permuted and arranged in y * t, the rows and columns in Σ would have to be permuted accordingly to generate Σ *. The matrix Σ * would then have a different Choleski factor: * * * p = Σ which would produce a different orthogonalized VMA representation. Therefore, the orthogonal VMA representation corresponding to the Choleski decomposition of variance covariance matrix of the reduced form disturbances is unique only given a particular ordering of the observable variables contained in y t. The triangular representation, which is sometimes referred to as Wold causal chain, is clearly a very particular one which cannot be considered suitable to every applied context. Sometimes, the researcher might have in mind different schemes for representing these instantaneous correlations, outside the straitjacket of the triangular structures. In recent literature, these alternative ways of modeling instantaneous correlation can be summarized in the following terms. Recent literature on the so-called structural VAR approach uses different ways of structuring the VAR model such as the K-model, the C-model, and the AB-model. p - K-model In the K-model, K is a (n*n) invertible matrix such that KA(L)y t = K ε t K ε t = et E(et) = 0 E ( e e ) = I t t n The K matrix premultiplies the autoregressive representation and induces a transformation on the ε t disturbances by generating a vector (e t ) of orthogonalized disturbances (its covariance matrix is not only diagonal, but also equal to the unit matrix I n ). Contemporaneous correlations among the elements of y are therefore modeled 170

182 through the specification of the invertible matrix K. the structural K-model can be thought of as a particular structural form with orthonormal disturbance vector. Note that, assuming we know the true variance covariance matrix of ε t terms from K ε t = e t K ε ε K = e e t t taking expectations, one immediately obtains K Σ K = I n t t the previous equation implicitly imposes n(n+1)/2 non-linear restrictions on the K matrix, leaving n(n-1)/2 free parameters in K. - C-model C is a (n*n) invertible matrix such that A(L)y t = ε t ε t = Cet E(et) = 0 E ( e e ) = I t t n In this particular structural model, we have a structural form where no instantaneous relationships among the endogenous variables are explicitly modeled. Each variable in the system is affected by a set of orthonormal disturbances whose impact effect is explicitly modeled via the C matrix. Sims (1988) stresses the point that there is no theoretical reason to suppose that C model should be a square matrix of the same order as K. If C were a square matrix, the number of independent (orthonormal) transformed disturbances would be equal to the number of equations. Many reasons lead us to think that the true number of originally independent shocks to our system could be very large. In that case, the C matrix would be a (n*m) matrix, with m much greater than n. In this sense, this research path is opposite to the one studied by the factor analysis, which attempts to find m (the number of independent factors) strictly smaller than n. the case of a rectangular (n*m) matrix C, with m>n, conceals a number of problems connected with the completeness of the model and the aggregation over agents (see Blanchard and Quah, 1989). In the C-model, the ε t vector is regarded as being 171

183 generated by a linear combination of independent (orthonormal) disturbances. This may have a different meaning than that of the K-model, where one is concerned with the explicit modeling of the instantaneous relationships among endogenous variables. As for the C-model, notice that from taking expectations, ε t = Ce t ε t ε t = Ce t e t C Σ = C C if, again, we assume to know Σ, the previous matrix equation implicitly imposes a set of n(n+1)/2 non-linear restrictions on the C matrix, leaving n(n-1)/2 free elements in C. - AB-model A,B are (n*n) invertible matrices such that: A A(L)y t = A ε t A ε t = Be t E(e t ) = 0 E ( e e ) = I t t n In this kind of structural model, it is possible to model explicitly the instantaneous links among the endogenous variables, and the impact effect on the orthonormal random shocks hitting the system. Notice that A matrix includes a transformation on the ε t disturbances vector, generating a new vector (Aε t ) that can be conceived as being generated by linear combinations (through the B matrix) of n independent (orthonormal) disturbance. Obviously this structure might have a different meaning than those of models K and C. Notice also that the AB-model can be seen as the most general parameterization nesting the C and K models as special cases. In fact, the C-model can be seen as a particular case of the AB-model, where A is chosen to be the identity matrix and the K- model corresponds to an AB-model with a diagonal B matrix. As in the previous case, from A ε t = Be t Aε t ε t A = BB 172

184 for Σ known, this equation again imposes a set of n(n+1)/2 non-linear restrictions on the parameters of the A and B matrices, leaving overall 2n 2 -n(n+1)/2 free elements. For more investigation of the relationship between Arab and international stock markets, the structural vector autoregression (SVAR) will be used to analyze the importance of interconnection between financial markets. The relationship between international markets (US, UK, and Japan) and each of the Arab stock markets will be investigated. The model incorporate the assumption that the returns on each of the three international markets, affect the returns on Arab markets but not vice versa. In other words, how do Arab stock markets response to shocks generated by international stock markets. The main purpose of SVAR is to obtain non-recursive orthogonolization of the error terms for impulse response analysis. In order to incorporate and capture the dynamic relationship among prospective returns, a block recursive model, similar to the SVAR model used by Zha (1999), Cushman and Zha (1997), and Berument and Ince (2005), will be used to examine the effect of a large economy s stock exchange movements (in our case US, UK, and Japan) on a small economy s stock exchange movements (each of the Arab stock markets). The foreign stock exchange index follows its own dynamics (an AR process is used as a proxy). Domestic stock exchange movements are affected by its own lag and movements of the foreign stock exchange. Therefore, the foreign stock market can be thought to have an exogenous effect on the domestic stock exchange. None of the lag variables of the domestic market determine foreign stock exchange; however, lag values and spot values of the foreign stock exchange affect domestic stock exchange. The VAR model has some advantages relative to the single equation model, since the VAR model allows dynamic interactions among variables and the VAR model has predictive power compared to the single equation model. VAR with block exogeneity is also used, since in conventional VAR; stock exchange movements of foreign markets are affected by domestic stock exchanges including lag values. By block exogeneity, this problem is overcome. The general specification of the identified VAR model of Cushman and Zha (1997) is: 173

185 A ( L) y( t) t = ε (5-19) in which, the A(L) is an mxm matrix polynomial in the lag operator L, y(t) is the mx1 observations vector and ε t is the mx1 vector of structural disturbances. Equation (5-20) shows the specification of the model. y1( t) y ( t) = y ( t), A11 ( L) 0 A ( L) = A ( L) A ( L), ( t) ( t) ( ) t ε1 ε = (5-20) ε 2 in equation (5-20), it assumed that ε(t) is uncorrelated with y(t-j) for j>0 and A(0) is nonsingular. Block exogeneity is represented by A 12 (L) in the matrix, which is zero. This means that y 1 (t) is exogenous to the second block both simultaneously and also for lagged values. The observation matrices are such that y 1 = [Foreign stock exchange], y 2 = [Domestic stock exchange]. 5-2 Transmission of stock prices movements between Arab stock markets Capital markets across countries or regions may exhibit varying degree of integration (or segmentation). Theoretically, market linkages primarily stem from the low of one price that identical assets; physical or financial, should bear the same price across countries after adjusting for transaction costs. Rational (well-informed) investors would, or perhaps should, arbitrage away price disparities, leading to more integrated markets. For the last few years, the development of financial markets in the Middle East region has opened a new era of mobility of financial resources, whereby flow of private capital has assumed an increasing role as a source of finance for these markets. More favorable developments have been taking place in most Arab stock markets to attract regional investors. Moreover, countries that share geographical proximity and have similar groups of investors are more than likely to have markets that influence each other. And when a stock is dually listed in two countries, shocks in one market can be transmitted to the other market through the security, plus investors in one market may react directly and indirectly to an initial stock in another market. 174

186 In this sequence, questions of market integration among Arab tock markets themselves, are of concern both to regional investors, and companies in the region that make capital budgeting decisions. Specifically, if segmentation exists and a firm is forced to raise capital locally, then its cost of capital is likely to be higher than that of a company with unrestricted access to the regional and international capital markets. Therefore, one would expect the restriction to the local capital market, to raise a firm s cost of capital. The focus of this section will be firstly, to examine the relationship among Arab stock markets. Second, to what extent and how rapid shocks induced by innovations in one market, are borne by another markets. Third, we explore whether there is a dominant market among Arab markets that links all other markets and makes most of their independence. Furthermore, despite the fact that Arab countries enjoy several common features, there still exist some differences especially in natural resources, such as oil production. So the whole markets will be divided into two groups: oil production countries, represented by GCC stock markets, and non-oil production countries; which includes Jordan, Egypt, and Palestine. In order to investigate the long-run relationship among Arab markets, Johansen cointegration technique will be used, while Granger causality, coefficient-correlation and vector autoregressive (VAR) will be employed to trace the short-run dynamics Granger causality Causality is defined as in Granger (1969), where the Granger method determines to what degree a current endogenous variable; can be explained by past values of the variable and whether the explanatory power; can be improved adding lagged values of another exogenous variable. If this is the case, the exogenous variable is said to Granger cause the endogenous variable. The Granger causality is measured by estimating an unrestricted and a restricted version of the equation y t n n = α 0 + α i yt 1 + β t j xt j + ε t (5-21) i= 1 j= 1 175

187 and employing an F-test to determine if the parameters of the exogenous variable are significantly different from zero, i.e. if the exogenous variable Granger cause the endogenous variable. The constant is denoted by α 0 and ε t is a white noise error term Vector autoregression (VAR) The VAR technique as applied to a simultaneous equation system, estimates unrestricted reduced form equations with uniform sets of the lagged dependent variables of each equation as regressors. Because this approach sets no restrictions on the structural relationships of the economic variables, it avoids mis- specification problems. The VAR methodology is suitable when variables within the model are highly autocorrelated. Furthermore, the VAR approach enables us to analyze the speed of information transmission among variables in the system, which would provide insight into the dynamic nature of the interactions between markets. The VAR model can be expressed in its standard form as: R p ( t ) c + A( k ) R ( t 1) + e( t ) = k = 1 (5-22) Where R(t) is a 9x1 column vector of daily returns on the markets at time t. C is a 9x1 column vector of constant terms, A(k) is a 9x9 matrix of coefficients such that the (i, j)th component of A(k) measures the direct effect that a change in the ith markets has upon the jth market after k periods. In particular, the ith component of e(t) is the innovation of the ith market that cannot be predicted from the past returns of other returns in the system. e(t) is a 9x1 column vector of innovations such that ( ) = 0 E ( e it, e jt k ) = E, E ( ) = σ, E( e it e jt ) = σ ij e it e it i, and Thus, the innovations, e(t), are serially uncorrelated but can be contemporaneously correlated. To analyze the dynamics of the system, we trace out the system s moving average representation which may provide additional insight into the dynamic interactions among the returns in the VAR model (Sims 1980). Thus, the VAR 176

188 model of equation (5-22) is typically transformed into its moving average representation expressed as: R ( t ) B ( k ) e ( t k ) = k = 0 (5-23) Equation (5-23) indicates that R(t) is a linear combination of current and past one-stepahead forecast errors (i.e. e(t)). The (i, j)th component of B(k) reveals the response of the ith market return to a unit random shock in the jth market return after k periods. The moving average model of equation (5-23) enables us to compute the m-step-ahead for K=0 forecast error of R(t) at time t-m+1 which can be expressed as B( k) e( t k) to m-1. In addition, the variance decomposition of the forecast error gives us the percentage of unexpected variation in each market s return that is produced by shocks from other returns in the system. As stated earlier, the innovations, e(t) in equation (5-22) may be contemporaneously correlated, i.e. the covariance matrix of innovations is not diagonal. When innovations in market returns are contemporaneously correlated, a shock in one market may work through the contemporaneous correlations with innovations in other markets. It is customary to transform these correlations by orthogonalizing the innovations in the VAR system according to a pre-specified causal ordering. After the transformation, the above equation can be expressed as, R ( t ) c ( k ) u ( t k ) = k = 0 (5-24) Where the transformed innovations, u(t), are now uncorrelated with each other at all lags as well as contemporaneously. The moving-average representation of the VAR model provides a convenient framework for tracing the dynamics to shocks in the system. The (i, j)th component of C(k) in equation (5-24) represents the impulse response of the ith market in k periods after a shock of one standard error in the jth market. That is, if there is a unit shock in the innovation of the jth market in period t(u jt ), the value of the ith 177

189 market (R i ), changes by c ij,1 in the following period and by c ij,2, c ij,3 and so on in successive future periods. The VAR model also makes it possible to analyze the decomposition of forecast error variance thereby providing a measure of the overall relative importance of an individual market in generating variations in its own returns and in other markets. That is, the effect that each variable in the system has on itself and on each other variables over different time horizons can be measured by decomposing this forecast variance error. In summary, the VAR analysis provides information on two important aspects of the structure of interactions among the national stock markets: (i) if innovations in a particular market explain a substantial amount of return variations in other markets and cannot be accounted for by innovations in other markets, then the market is relatively influential to other markets; and (ii) if the impulse response of a market to a shock in another market tapers off quickly, then the transmission of information between these markets is relatively efficient. The VAR requires the determination of the appropriate lag structure in the system. We chose the lag structure using the Akaike Information Criterion (AIC) in conjunction with analyzing the estimated model s residuals so they do not exhibit any significant autocorrelation. 5-3 Empirical results The empirical analysis of the relationships among Arab stock markets indices; as an economic group, with international markets, and among Arab stock markets themselves; requires that several time series tests to be conducted. The first step requires that unit root tests be performed to determine whether the series are non-stationary in levels and stationary in first differences, that is, integrated of degree one. The second step is to use the cointegration test to determine whether these non-stationary series, have common long-run relationships. Evidence of cointegration rules out the possibility that the estimated relationships are spurious; thus, as long as the variables in a given VAR have common trends, causality (in Granger sense but not the structural sense) must exist in at least one direction. However, although cointegration implies the presence of causality, it does not identify the direction of causality between variables. The dynamic 178

190 Granger causality can be captured through the vector error correction (VEC) models, derived from the long-run cointegrating vectors. Furthermore, Engle and Granger (1987) show that in the presence of cointegration, a corresponding error-correction model representation always exist. The data used for cointegration test consists of daily indices prices for Arab stock markets and S&P 500 which has been used as a proxy for international markets. The data comes from daily figures, run from 1 st August December The length of this period is limited by the availability of data for all Arab stock markets included in this test Integration Integration for individual time series for each stock market index is tested by means of unit root tests, which investigate the presence of a stochastic trend in the individual series. The results of the three unit root tests (ADF, PP, and KPSS) are presented in table The indices used for the 13 markets are: Kuwait (KSEI); Jordan (JSMI); Bahrain (BSEI); Dubai (DFMI); Egypt (EFMI); Oman (OSMI); Abu Dhabi (ABSMI); Palestine (PSEI); Saudi Arabia (SAUDI); Japan, Nikkei 225 (JAPANI); US, S&P 500 (USAI); UK, FTSE100 (UKI); and oil prices (WTI) which is crude stream produced in Texas and Southern Oklahoma. 179

191 Table 5-1 Unit Root Tests for Each Individual Series, Both in Levels and First Differences Levels First Difference Variables ADF Lags PP Lags KPSS BW ADF Lags PP Lags KPSS BW KSEI IT IT IT I I I 9 JSMI IT N IT I I I 1 BSEI I I IT I I I 24 DFMI IT IT IT IT IT IT 8 EFMI 0.22 IT IT IT IT IT IT 5 OSMI IT N IT N N I 15 ABSMI 2.95 N N IT I I I 9 PSEI I I IT N I I 3 SAUDI -1.8 IT IT IT IT IT IT 17 JAPANI IT IT IT I N I 11 USAI 1.95 N N IT I I I 27 UKI I N IT N N I 15 WTI N N IT N N I 31 Nate: All variables are in natural logs. All unit root tests agree that all variables are I (1). The lag selection is based on the lowest values for AIC criterion. Superscript N stands for no intercept and no trend. I for intercept only and no trend, and IT for both intercept and trend. Significant statistics are in bold, and the series are stationary. BW stands for bandwidth. The results of these three tests show that all variables appear to be non-stationary in levels and stationary in the first differences or integrated of the first degree Long-run relationship (cointegration test) Here we present the cointegration results to test the long-run relationships at different levels, and for several sets of VARs: VAR-10 which includes all Arab and US stock markets, and designed to investigate the long-run cointegrating relation between Arab and international stock markets, VAR-9 which includes all Arab stock markets, so as to investigate the cointegrating relation between Arab stock markets themselves. While, after dividing Arab markets into two subgroups; oil production countries which include the GCC markets and non-oil countries, VAR-6 consisting of the six GCC stock markets, whereas VAR-3 contains non-oil countries (Jordan, Egypt, and Palestine). Several criterions (Akaike, Schwarz, and likelihood ratio) have been used to determine the appropriate lag length for each VAR. To select the adequate deterministic component for the cointegration and VAR equations for each VAR, we exclude specification 1 of the deterministic component (that is, no intercept and no trend in either 180

192 the data or the cointegrating relations) for all VARs because according to Johansen, this specification is rare and does not usually provide the minimum to account for the presence of deterministic components. We also exclude specification 5 (quadratic trend with intercept), because it is also a rare case and produces implausible forecasts out of sample. Next, to choose a specification from the remaining specifications 2, 3, and 4, we use the Schwarz criterion to determine the suitable specification. As table 5-2 shows, the selected deterministic specification for VAR-3 is specification 2 (i.e. data have no deterministic trend, but the cointegrating equation have intercept). This selection also holds for VAR-6 and VAR-9, while for VAR-10, the selected deterministic component is specification 4 (both data and the cointegrating equations have linear trend). 8 Table 5-2 Number of Cointegrating Relations for Four VARs Models Specifications VAR-3 VAR-6 VAR-10 VAR-9 None a Intercept b 0* 1* 1 1* Linear trend c Linear trend d 0 0 0* 0 Quadratic trend e No. of lags(levels) Observations Notes: All variables are expressed in natural logarithms. Astrisks indicate the selected deterministic specifications for each VAR. a Data have no deterministic trend, and the cointegrating equations do not have intercepts. b Data have no deterministic trend, but the cointegrating equations have intercepts. c Data have linear trend, but the cointegrating equations have intercepts only. d Both data and the cointegrating equations have linear trend. e Data has quadratic trends, but the cointegrating equations have linear trends. VAR-3 :ESMI, PSEI and JSMI; VAR-6: MSMI, KSEI, DFMI, BSEI, ABSEI and SAUDI; VAR10: MSMI, KSEI, DFMI, BSEI, ESMI, PSEI, ABSEI, SAUD, JSMI and USAI; VAR-9: MSMI, KSEI, DFMI, BSEI, ESMI, PSEI, ABSEI, SAUD AND JSMI Table 5-3 presents the results of Johansen-Juselius cointegration test, both trace and maximum eigenvalue tests for each of the 4 VARs models. Based on these specifications, table 5-3 suggests that VAR-6 and VAR-9 have one cointegrating relation, 8 The indices used for the each market are: Kuwait (KSEI); Jordan (JSMI); Bahrain (BSEI); Dubai (DFMI); Egypt (EFMI); Oman (OSMI); Abu Dhabi (ABSMI); Palestine (PSEI); Saudi Arabia (SAUDI); US, S&P 500 (USAI). 181

193 while the results of Johansen-Juselius cointegration test; indicate the absence of any cointegrating relation between Arab and international stock markets (VAR-10). The results also indicate no cointegrating relation between the non-oil production countries Jordan, Egypt and Palestine (VAR-3). The presence of one cointegrating relation among Arab stock markets as a group (VAR-9), GCC stock markets; oil production countries; (VAR-6), suggests long-run relationships among variables. These results are constant with the existing literature; fore example, Kasa (1992) finds one cointegrating relation among monthly stock indices of the United States, Japan, United Kingdom, Germany and Canada. Francis and Leachman (1998) also find one cointegrating relation for United States, Japan, United Kingdom, and Germany for the same period. Furthermore, Bassler and Yang (2003) find only one cointegrating relation in the nine largest countries in terms of market capitalization. Regarding the Middle East region, Hammoudeh and Al-Eisa (2004) find two cointegrating relations between five GCC countries, Darrat et al. (2000) find that cointegrating relation exists between three Arabian equity markets, Jordan, Egypt, and Morocco, but these markets found to be isolated from international markets. Maneschiold (2005) finds one cointegrating relation between USA and Egypt at the general index level, related to the industry sub-index but not to the financial or services sub-indices Short-run relationship between Arab and international stock markets The results of cointegration test indicate that, there is no cointegrating relation between Arab and international stock markets in the long-run. The purpose of this section is to investigate the short-run relationship between Arab and international stock markets, through analyzing how shocks generating by US, UK, and Japan markets, affect each of the Arabian markets using SVAR technique. The S&P 500, FTSE100, and Nikkei 225 are used to represent US, UK, and Japan stock markets, respectively. The data includes daily observations for different time horizons. 9 9 The daily data for international stock markets S&P 500, FTSE100, and Nikkei 225 are obtained from Yahoo. Finance website on the net through: 182

194 Table 5-3 H 0 =Number of Trace Test Maximum Eigenvalue Test Cointegrating Vectors Statistics C.V (5%) C.V (1%) Statistics C.V (5%) C.V (1%) A. Cointegrating System: VAR-6 b None * * At most ** At most At most At most At most B. Cointegrating System: VAR-9 b None * ** At most At most At most At most At most At most At most At most C. Cointegrating System: VAR-3 b None At most At most D. Cointegrating System:VAR-10 d None At most At most At most At most At most At most At most At most At most Notes: All variables are expressed in natural logarithms. **(*) denotes rejection of the hypothesis at the 5%(1%) level b Data have no deterministic trend, but the cointegrating equations have intercepts. d Both data and the cointegrating equations have linear trend. VAR-3 :ESMI, PSEI and JSMI; VAR-6 : MSMI, KSEI, DFMI, BSEI, ABSEI and SAUDI; VAR10 : MSMI, KSEI, DFMI, BSEI, ESMI, PSEI, ABSEI, SAUD, JSMI and USAI; VAR-9 : MSMI, KSEI, DFMI, BSEI, ESMI, PSEI, ABSEI, SAUD AND JSMI Johansen-Juselius Cointegration Test Results The critical values for the test statistics have been generated by Monte Carlo methods and tabulated by Osterwald-Lenum (1992). 183

195 Moreover, it is assumed that US, UK, and Japan stock exchanges performance, is not affected by Arab stock markets; however, Arab markets are affected by both its own dynamics and the three international markets. This assumption is reflected in the specification by using block recursive VAR model described in section The results are presented in appendix 3. figure 1 in appendix 3 reports the impulse response functions for 20 days concerning, how do Arab markets returns respond to onestructural standard deviation shock to each of the three international markets, whilst tables 1 and 2 in appendix 3 show the variance decomposition and the impulse response functions, respectively. In general, it is important to recognize that shocks originated in international markets, have a marginal effect on Arab stock markets returns. More specifically, table 1 provides the variance decomposition of the 2-, 6-, and 10 days a head forecast errors for each Arab stock markets, accumulated for by innovations in US, UK, and Japan. The results indicate that all markets are strongly exogenous in the sense that; the percentage of the foreign explanatory power as indicated by US, UK, and Japan, is very weak, reaching 3.5% in the best cases (the case of Egypt). Nevertheless, one can find that UK exerts the most influence effect on Arab stock markets, since it has the most foreign explanatory power on 4 Arab markets (Bahrain, Saudi, Palestine, and Jordan), while US has the most explanatory power of returns variation in three Markets (Oman, Kuwait, and Dubai). With Japan to be the least influencing market, since it has an explanatory power only on Abu Dhabi and Egypt. Moreover, it is apparent that a shock originated in UK has a persistent impact on Bahrain, Saudi, Palestine, and Jordan with duration up to 8 days in average. Among these markets, only Jordan responds positively to UK shock. However, Oman, Kuwait, and Dubai show a memory of 5 days to absorb shock originated in US market, it seems that these markets react quickly and relatively efficient. Since their response tapers-off and decline rapidly with a positive reaction from Dubai only. Finally, Egypt and Abu Dhabi react positively and quickly, to a shock generated by Japan stock market with duration up to 4 and 6 days for Abu Dhabi and Egypt, respectively (see appendix 3, figure1 and table 2) Short-run relationships among Arab stock markets 184

196 The presence of cointegrating relations in VAR-9 (all Arab stock markets) and VAR-6 (GCC markets), suggests that causality among the variables in these systems exists; in at least one direction. As a sequence, this section investigates the existing shortrun relationships among Arab stock markets Granger causality test Granger causality test has been conducted, to investigate the interdependence among the 9 Arab markets. The results are included in table 5-4. A lag order of seven (i.e. m=7) is used. It is expected that 7 lags (7 days) should be long enough to complete the transmission process, as similar results are obtained for higher-order lags used. Table 5-4 shows that there are some causal relationships observed among the nine markets, with Egypt exerting significant influence and leading Bahrain and Kuwait, while Bahrain leads Saudi but not vice versa, Another Granger causal relation exists from Oman to Egypt and from Jordan to Palestine. Table 5-4 Granger Causality Test for Arab Stock Markets 1st July, 2001 to 24 July, 2003 Dependent variables Bahrain Oman Kuwait Saudi Jordan Egypt Palestine Dubai AbuDhabi Bahrain 1.79(0.08) 0.56(0.78) 2.58(0.01) 0.82(0.57) 1.36(0.22) 1.53(0.15) 1.67(0.11) 0.82(0.57) Oman 1.82(0.08) 1.2(0.3) 1.02(0.42) 0.88(0.52) 2.17(0.03) 2.02(0.06) 0.47(0.85) 0.66(0.7) Kuwait 0.62(0.74) 0.8(0.59) 0.63(0.73) 0.95(0.46) 0.87(0.52) 0.58(0.77) 1.65(0.12) 0.95(0.46) Saudi 0.38(0.92) 0.55(0.79) 0.57(0.77) 1.35(0.22) 1.35(0.85) 1.26(0.26) 0.48(0.84) 1.14(0.34) Jordan 1.38(0.21) 1.5(0.16) 0.41(0.90) 1.65(0.12) 1.37(0.21) 2.06(0.04) 0.54(0.8) 1.09(0.37) Egypt 2.21(0.03) 1.11(0.35) 2.33(0.02) 1.1(0.36) 1.29(0.25) 0.49(0.84) 0.22(0.98) 0.27(0.97) Palestine 1.88(0.07) 1.64(0.12) 0.71(0.66) 0.59(0.76) 0.73 (0.64) 0.74 (0.63) 0.52(0.82) 0.6 (75) Dubai 0.44(0.87) 1.66(0.11) 1.83(0.07) 1.77(0.09) 0.3(0.95) 0.22(0.98) 0.76(0.62) 0.16(0.99) AbuDhabi 0.71(0.66) 0.88(0.51) 1.28(0.25) 0.68(0.69) 1.28(0.26) 0.85(0.54) 0.71(0.66) 0.86(0.54) The numbers report the F -statistics for testing the null hypothesis that all 7 lags of the left column do not Granger-cause the dependent variable. P -values are between brackets. Furthermore, the correlation coefficient analysis between daily Arab markets indices is presented in table 5-5. The correlation between Arab stock markets indices, are generally low and close to zero for most cases, indicating a low interdependence between these indices. 185

197 Table 5-5 Correlation Coefficient Between Daily Arab Markets' Returns, 1 st July, 2001 to 24 July, 2003 Saudi Oman Kuwait Dubai Bahrain AbuDhabi Egypt Jordan Palestine Saudi Oman Kuwait Dubai Bahrain AbuDhabi Egypt Jordan Palestine Causality and error-correction models (VEC) The results for long-run relationship (cointegration analysis), indicate the existence of one long-run cointegrating relation in VAR-9 and VAR-6. The existence of these long-term relationships indicates the subsistence of causality among variables. One way that causality may emerge in a VAR is through the cointegrating equations or the error-correction terms of the associated VEC model. Testing the statistical significance of the adjustment coefficients of these terms, amounts to testing if the long-run relationships derive the endogenous variables to convergence to equilibrium over time, such testing of the adjustment coefficients is known as testing weak exogeneity of the endogenous variables, with respect to the parameters of the cointegrating equations. Therefore, we will first present and interpret the cointegrating equations of the VEC models. Table 5-6 presents the cointegrating equations for both VAR-9 and VAR-6, and indicates that Kuwait has; by far, the greatest relative influence on Arab and GCC indices. Thus, in the absence of global factors, such as oil prices, the Kuwaiti market dominates the long-run relation with other GCC and Arab markets, followed by Saudi Arabia. These results could be surprised, since one expects that the Saudi market to be the leader in the long run, since Saudi s economy is the largest among the Arabian countries and its stock market makes up about 50% of the total Arab markets capitalization followed by Kuwaiti economy. The results may suggest that local factors (such as cross-listing of Kuwaiti companies on both UAE and Bahraini stock markets) as well as global factors (i.e. oil prices) also have a strong influence on the long-run 186

198 relationship that derive the variables in the system to equilibrium over time. Figure 5-1 shows the cointegrating relations for the two systems. Table 5-6 Cointegrating Equations of the VEC Models for VAR-9 and VAR-6, 1/8/ /12/2004 Model CE MSMI KSEI DFMI BSEI ESMI PSEI ABSEI SAUDI JSMI C VEC-9 CE a a b b (0.57) (0.776) (1.771) (0.589) (0.447) (0.879) (0.685) (0.726) (13.632) VEC-6 CE a a b (0.225) (0.296) (0.661) - - (0.333) (0.248) - (4.761) Notes: CE stands for a cointegrating equation. VEC-9 is the VEC model for the VAR-9 and VEC-6 is the VEC model for the VAR-6. a b and c represent statistical significance at 1%, 5%, and 10% respectively. Figure Cointegrating relation VEC-9 Cointegrating relation VEC-6 Next, before presenting the estimated VEC models for the two VARs and tests for weak exogeneity, we should test whether the variable of the VARs inter the cointegrating equations significantly. Table 5-7 shows that only Kuwait and Saudi inter the cointegrating relation significantly in VAR-9 while for VAR-6; in addition to Kuwait and Saudi, Oman inters the equation significantly. This means that there are common factors that make Kuwaiti, Saudi, and Omani indices, as a group form one long-relationship. Furthermore, the finding of VAR-9 VEC model is presented in table 5-8. Broadly, the short-term relationships between Arab markets found to be weak. Since on a daily basis, there are two-way directional relation between Saudi and Jordan, one-way relation from Oman to Bahrain, from Egypt to Oman, and from Abu Dhabi to Saudi. 187

199 Table 5-7 Significant of Zero Restrictions on Coefficients of Cointegrating Equations of the VEC Models of VAR-9 and VAR-6 Market VAR-9 VAR-6 Chi-Square Probability Chi-Square Probability MSMI KSEI DFMI BSEI ESMI PSEI ABSEI SAUDI JSMI Notes: Bolds numbers indicate that the LR tests reject the null hypothesis that the ith endogenous variable does not enter the cointegrating equation significantly.which means that these variables form long run equilibrium relationships. Table 5-8 VEC Model for 9 Arabian Indices in the VAR-9, 1/8/ /12/2004 Model D(MSMI) D(KSEI) D(DFMI) D(BSEI) D(ESMI) D(PSEI) D(ABSEI) D(SAUDI) D(JSMI) ECT a b a b c a a D(MSMI(-1)) a c D(KSEI(-1)) a c D(DFMI(-1)) D(BSEI(-1)) b D(ESMI(-1)) a a D(PSEI(-1)) a D(ABSEI(-1)) b c D(SAUDI(-1)) c 0.08 b D(JSMI(-1)) b 0.17 a Stat. P.value Log Likelihood Akaike Information Criteria Schwarz Criteria Serial Crrelation LM Stat.(up to lag 4 ) Skewness Kurtosis Normality White test Notes: ECT stands for the error-correction terms in the VEC equations. The number of lags is based on the Schwars criteria. The statistics for skewness, kurtosis, normality, and the White test are chi-squares testing the null hypothesis. The statistics for the normality tests base are based on Doornik-Hansen orthogonalization method. All variables are first differences of logs. a.b and c represent statistical significance at the 1%, 5% and 10% levels respectively. 188

200 Another interesting finding from the VEC-9 model is that, the own short-run adjustment term for Saudi is negative. This result may suggest that it takes a while for the Saudi market to cool off after getting heated by hot hands of its momentum traders. Moreover, the estimates show that Dubai index lacks linkages with other Arab markets indices, even with its own lags. The estimated VEC model for VAR-6 (GCC markets) is presented in table 5-9. The results indicate that Kuwaiti market can be considered as a leader among GCC markets. Since it can predict both Bahrain and Abu Dhabi markets, the results also indicate that two-way directional relationship exists between Kuwait and Bahrain, Abu Dhabi and Dubai markets. Whereas, Oman and Saudi markets have the weakest links with other GCC markets on the short-run relationship. Moreover, the Wald test for the adjustment coefficients of the error-correction terms, which measures deviations from the long-run equilibrium indicating that, for VEC-9, five among the nine markets including in the model (Kuwait, Bahrain, Egypt, Palestine, and Abu Dhabi) are weakly exogenous (see table 5-10). In other words, it appears that Oman, Saudi, and Dubai equations contain all the long-run information, since these are the only equations that their equilibrium adjustment parameters inter the system significantly according to Wald test. While for the VEC-6 model, the results of Wald test indicate that Kuwait, Bahrain, and Abu Dhabi were weakly exogenous, implying that these markets do not have the tendency to restore equilibrium and take the brunt of the shocks to the system, whereas the Saudi market contains the long-run information since its equation contains the large significant equilibrium adjustment parameter. The results for the interrelation between GCC stock markets in the short run (VEC-6), are incompatible with those of Assaf (2003), since he finds; using VAR analysis for weekly data, that Bahrain plays a dominant role in influencing the GCC markets. While the results for GCC stock markets indicate that Kuwait can be considered as a dominant market that influences the GCC markets in the short-run horizon. 189

201 Table 5-9 VEC Model for 6 GCC Indices in the VAR-6, 1/8/ /12/2004 Model D(MSMI) D(KSEI) D(DFMI) D(BSEI) D(ABSEI) D(SAUDI) ECT a c a a D(MSMI(-1)) a D(MSMI(-2)) D(KSEI(-1)) a D(KSEI(-2)) b b D(DFMI(-1)) D(DFMI(-2)) c D(BSEI(-1)) b D(BSEI(-2)) c D(ABSEI(-1)) b D(ABSEI(-2)) a D(SAUDI(-1)) D(SAUDI(-2)) b Stat. P.value Log Likelihood Akaike Information Criteria Schwarz Criteria Serial Crrelation LM Stat.(up to lag 5 ) Skewness Kurtosis Normality White test Notes: ECT stands for the error-correction terms in the VEC equations. The number of lags is based on the Schwars criteria. The statistics for skewness, kurtosis, normality, and the White test are chi-squares testing the null hypothesis. The statistics for the normality tests base are based on Doornik-Hansen orthogonalization method. All variables are first differences of logs. a.b and c represent statistical significance at the 1%, 5% and 10% levels respectively. Table 5-10 Weak Exogeneity Tests of the Endogenous Variables in the VEC Models of VAR-9 and VAR-6, 1/8/ /12/2004 Market VEC-9 VEC-6 Chi-Square Probability Chi-Square Probability MSMI KSEI DFMI BSEI ESMI PSEI ABSEI SAUDI JSMI Notes: Bolds numbers indicate that the LR tests do not reject the null hypothesis that the i th endogenous variable is weakly exogenous with respect to the β parameters, implying that it does not adjust to restore equilibrium after a shock hits the system. 190

202 5-3-5 Dynamic relationship between GCC stock markets and oil prices The last two years have witnessed a sharp increase in oil prices as a result of the increase in the international demand, among other things. It is known that this increase negatively affects the economic development of industrial countries in particular and that of other countries in general. This, in turn, leads to raising the inflation rate and unemployment. Economic sectors in many countries have been influenced, especially stock markets. Gulf Cooperation Council countries (GCC) are among the most important oil producing countries, except Bahrain and Oman, the other four of these (Saudi Arabia, Qatar, Kuwait and UAE) are members in the Organization of Petroleum Exporting Countries (OPEC). At the end of 2003, these countries collectively accounted for about 21% of the world s 68 million barrels a day of total production. They possess 43% of the world s billion barrels of oil proven reserves 10. Producing and exporting oil plays a crucial role in determining foreign earnings and government s budget revenues and expenditures for those countries; thus they are the primary determinant of aggregate demand, which in turn affects the domestic price levels as well as all aspects of daily economic life. In addition, increase in oil prices causes increase in the trading volume in the stock markets according to cash surplus. This shows the importance of studying the relation between increase in oil prices and stock markets in GCC countries where oil price has reached $ 70 a barrel, while it was $ on 27 May There are several studies and research articles which investigate the relation between international financial markets and others examined the link between spot and future petroleum prices. However, few studies have looked into the relation between oil spot/future prices and stock markets. Such studies concentrated on countries such as Canada, Germany, Japan, UK, and USA. The overall literature on the links between oil markets and financial markets is very limited; Johnes and Kaul (1996) investigate the relation of the U.S., Canadian, Japanese, and U.K stock prices to oil price shocks using quarterly data. Utilizing a standard cash-flow dividend valuation model, they find that for the United States and Canada, this relation can be accounted for entirely by the impact of 10 See the Uniform Arabian Economic Report 2004, Arab Monetary Fund (AMF), Abu Dhabi. 191

203 oil shocks or real cash flows. The results for Japan and the United Kingdom were not as strong. Moreover, Huang et al. (1996) use an unrestricted vector auto regression (VAR) model to examine the relationship between daily oil future returns and daily U.S stock returns. They find that the oil futures returns lead some individual oil company stock returns, but they do not have much impact on broad-based market indices such as the S&P 500. Sadorsky (1999), using monthly data examines the links between the U.S. fuel oil prices and the S&P 500 in an unrestricted VAR model that also include the short-term interest rate and industrial production. He finds that oil prices movements are important in explaining movements in broad-based stock returns. Papapetrou (2001), employing an error correction representation of a VAR macroeconomic model and using monthly data for Greece, concludes that oil prices are important in explaining stock price movements. Hammoudeh and Aleisa (2002) examine the links between oil-exporting countries, including Bahrain, Indonesia, Mexico, and Venezuela, using monthly data, and find spillovers from the oil markets to the stock indices of these countries. However, one study (Hammoudeh and Al-Eisa, 2004) examines the relation between oil prices and stock markets in GCC countries. For daily data during the period , they find that the Saudi market is the leader among GCC markets and can predict-and be predicted by oil future prices. The purpose of this section is firstly, to investigate the dynamic relationship between oil prices and GCC stock markets both on the long and short-run, second, to investigate how oil prices affect returns volatility in GCC markets, finally and most important, this section will investigate the impact of the increase in oil prices on GCC stock markets, and trying to identify the nature of the dynamic relationship between oil prices and GCC markets Oil prices and GCC markets volatility In order to investigate the effect of oil prices on the volatility of returns in GCC markets, GARCH (1,1) will be estimated, while oil prices will be added as an additional regressor in the conditional variance equation, such as: 192

204 25) R β 0 β + ε (5- t = + 1R t 1 ε t N(0,h t ) t h t = 0 1 t 1 1 t α + α ε + β h w oil (5-26) where R t is log (P t /P t-1 ); and P t is the stock price at time t. Equation (5-26) models the variance of the unexpected returns, ε t, as GARCH (1,1) process; and oil is oil return log(p t /P t-1 ). While for oil spot prices, we use the spot oil prices (WTI), which is the crude stream produced in Texas and South Oklahoma that is traded in domestic spot market at the Cushing Center. Table 5-11 shows the results of the estimated model, while table 4 in appendix 2 presents the results of diagnostic tools for the standardized residuals of GARCH (1,1) model with oil prices. The coefficient of oil returns presented in table 5-11; w, is found to be highly significant for all GCC stock markets except Kuwait. Table 5-11 GARCH (1,1) Model for GCC Daily Returns with Oil Returns as a Regressor in the Variance Equation Market Obs. α 0 α 1 β 1 α 1 +β 1 ω AbuDhabi /7/01-31/12/ Bahrain /1/91-3/6/ Dubai /3/00-31/12/ Kuwait /6/01-9/3/ Oman /2/97-13/10/ Saudi /1/94-14/3/ Significance levels are in italics. A Chi-square (χ 2 ) tests (α 1 +β 1 ) = 1. The estimated variance equation is : 2 h t = a 0 + a 1ε t 1 + β 1h t 1 + ω oilr 193

205 Note that, volatility persistence for all markets does not change with the addition of oil returns (see table 4-12 in section 4-5-2). The results indicate that oil prices play a significant role in affecting GCC markets volatility but not Kuwait, since it appears that for Kuwaiti stock market, other factors than oil prices affect its volatility Long-run relationship among GCC stock markets and oil prices The GCC countries are oil-dependent as well as economically and politically similar. The forces that commove their stock markets are basically the forces that move the oil prices. As a result, one would expect cointegrating relations among the markets of countries that have had a cooperation council since 1981, and are now aiming to establish a monetary union with a single currency in Table 5-12 presents the results of Johansen cointegration test for 6 GCC indices with oil prices. The result of the trace test indicates the existence of one long-run cointegrating relation between the variables. While cointegrating equation is presented in table Table 5-12 Johansen-Juselius Cointegration Test Results H 0 =Number of Trace Test Maximum Eigenvalue Test Cointegrating Vectors Statistics C.V (5%) C.V (1%) Statistics C.V (5%) C.V (1%) None ** At most ** At most At most At most At most At most Notes: All variables are expressed in natural logarithms, where 3 lags have been used to estimate the VAR. **(*) denotes rejection of the hypothesis at the 5%(1%) level Data have no deterministic trend, but the cointegrating equations have intercepts. VAR-7 : MSMI, KSEI, DFMI, BSEI, ABSEI, SAUDI and OILPI. Table 5-13 Cointegrating Equations of the VEC Model for VAR-7, 1/8/ /12/2004 Model CE MSMI KSEI DFMI BSEI ABSEI SAUDI C OILPI VEC-7 CE b b (1.138) (1.426) (3.228) (1.618) (1.248) ( ) (1.009) Notes: CE stands for a cointegrating equation. VEC-7 is the VEC model for the VAR-7. a b and c represent statistical significance at 1%, 5%, and 10% respectively. 194

206 The oil price index found to have significant influence on the long-run equilibrium after Kuwait stock market, which dominate the long-run relation among GCC and oil prices. The findings of the VAR-7 VEC model (table 5-14); suggest that 4 out of 6 GCC markets (Oman, Kuwait, Bahrain, and Saudi) can predict and explain the future oil prices movements in the short-run, while oil prices can explain both Saudi and Dubai indices only. The existing two-way directional relation between oil prices and Saudi market; could be explained by the fact that Saudi Arabia is the largest oil exporter and has the largest oil reserves. Table 5-14 VEC Model for 6 GCC and Oil Price Indices in the VAR-7, 1/8/ /12/2004 Model D(MSMI) D(KSEI) D(DFMI) D(BSEI) D(ABSEI) D(SAUDI) D(OILPI) ECT a a a b D(MSMI(-1)) a D(MSMI(-2)) b D(KSEI(-1)) b D(KSEI(-2)) b a D(DFMI(-1)) D(DFMI(-2)) c D(BSEI(-1)) c b b D(BSEI(-2)) b D(ABSEI(-1)) c c D(ABSEI(-2)) a D(SAUDI(-1)) D(SAUDI(-2)) b c D(OILPI(-1)) a D(OILPI(-2)) a c Stat. P.value Log Likelihood Akaike Information Criteria Schwarz Criteria Serial Crrelation LM Stat.(up to lag 7 ) Skewness Kurtosis Normality White test Notes: ECT stands for the error-correction terms in the VEC equations. The number of lags is based on the Schwars criteria. The statistics for skewness, kurtosis, normality, and the White test are chi-squares testing the null hypothesis. The statistics for the normality tests base are based on Doornik-Hansen orthogonalization method. All variables are first differences of logs. a.b and c represent statistical significance at the 1%, 5% and 10% levels respectively. 195

207 Moreover, the Wald test for the adjustment coefficients of the error-correction term, is presented in table 5-15 and indicates that all variables in the system are weakly exogenous except oil prices and Dubai index. It appears that, oil and Dubai indices contain the information of the long-run equilibrium in the system. Table 5-15 Weak Exogeneity Tests of the Endogenous Variables in the VEC Model of 1/8/ /12/2004 Market VEC-7 Chi-Square Probability MSMI KSEI DFMI BSEI ABSEI SAUDI OILP Notes: Bolds numbers indicate that the LR tests do not reject the null hypothesis that the i th endogenous variable is weakly exogenous with respect to the β parameters, implying that it does not adjust to restore equilibrium after a shock hits the sysytem. These results are inconsistent with those obtained by Hammoudeh and Al-Eisa (2004), since they found two equilibrium relations between GCC stock markets and oil future prices, while Saudi market found to be the leader followed by Bahraini and United Arab Emirates (UAE) The rise of oil prices and GCC stock markets This section will investigate the direct effect of the increasing oil prices on GCC stock markets, during the period which witnessed unprecedented sharp raise, especially through the last two years. Daily data for five GCC (Bahrain, Kuwait, Oman, Saudi Arabia, and Abu Dhabi) and oil prices will be used, the data runs from 25 May 2001 to 24 May The WTI described in section will be used as a proxy for oil prices. To facilitate the investigation of how the raise in oil prices affects GCC stock markets and the dynamic relation between them, the whole period has been divided into 11 Dubai stock market was excluded according to the shortage of data for the later period. 196

208 two sub-periods (event study), and a vector autoregression (VAR) system will be estimated for each period. The first period spanning from 25 May 2001 to 23 May 2003, while the second one from 27 May 2003 to 24 May 2005, which includes the stunning rise in oil prices. Since at the end of the second period, the oil prices reached US$ per barrel compared to on 23 May Table 5-16 presents the estimation of the VAR system for five GCC stock markets returns and oil return for the first sub-period. The results indicate that there is no any relationship between oil prices and the five GCC stock markets on a daily basis. The oil prices can not predict- or be predicted by any of the five GCC markets 12. However, during the second period; and after the rise in oil prices, the results changed dramatically. Table 5-17, which shows VAR estimation for the second period, indicates that oil prices can predict all GCC stock markets but not Abu Dhabi. While oil prices can be predicted by both Saudi and Omani stock markets. The results of the VAR system for the second sub-period reflect the significant role that the sharp increase in oil prices plays, which has, in turn, brought about enhancing the predictive power of oil prices on those markets in comparison with the first sub-period that preceded the sharp increase in oil prices. The results should not seem strange if we take into consideration that these countries essentially depends; in varying degree, on oil, and that one of them is Saudi Arabia, the world s biggest oil exporting country with its largest oil reserve in the world. - Variance decomposition The variance decomposition analysis measures the percentage of the forecast error of a market return that is explained by another market or oil return. It indicates the relative impact that one market has upon another market and oil return within the VAR system. The variance decomposition enables us to assess the economic significance of this impact as a percentage of the forecast error for a variable sum to one. The orthogonolization procedure of the VAR system decomposes the forecast error variance, 12 These results contradict those obtained from cointegration analysis during the period 1 August December 2004 presented in table (3-14), since the finding of VEC-7 model indicates that in the short run, 3 out of 6 GCC markets have predictive power on oil prices, while both Dubai and Saudi markets can be predicted by oil prices. 197

209 the component that measures the fraction in stock return of a particular market explained by innovations in each of the six indices. Table 5-18 provides the variance decomposition of the 3-, 6-, 9 days ahead forecast errors of each index, accumulated for by innovations in each of the six indices for the first sub-period. The results indicate that all markets and oil returns are strongly exogenous in the sense that the percentage of the error variance accounted for by their innovations around 98%. The percentage of the foreign explanatory power, as indicated by the foreign column, is weak, reaching in the best cases 3%. Table 5-16 VAR System for GCC Stock Markets and Oil Returns for the First Sub- Period 25May, 2001 to 23May, 2003 BAHRAIN OIL OMAN KUWAIT ABUDHABI SAUDI BAHRAIN(-1) BAHRAIN(-2) OIL(-1) OIL(-2) OMAN(-1) OMAN(-2) KUWAIT(-1) KUWAIT(-2) ABUDHABI(-1) ABUDHABI(-2) SAUDI(-1) SAUDI(-2) C stat. P-value PTA (12) LM test (12) Notes: Numbers in italic represent P-values. VAR system has been estimated with 2 lags according to Akaike information selection criterion, C represents a constant in the VAR system. PTA represents Residual Portmanteau Tests for Autocorrelations up to lag 12. LM represents Residual Serial Correlation LM Tests up to lag

210 Table 5-17 VAR System for GCC Stock Markets and Oil Returns for the Second Sub-Period 27May, 2003 to 24May, 2005 BAHRAIN OIL OMAN KUWAIT ABUDHABI SAUDI BAHRAIN(-1) BAHRAIN(-2) BAHRAIN(-3) OIL(-1) OIL(-2) OIL(-3) OMAN(-1) OMAN(-2) OMAN(-3) KUWAIT(-1) KUWAIT(-2) KUWAIT(-3) ABUDHABI(-1) ABUDHABI(-2) ABUDHABI(-3) SAUDI(-1) SAUDI(-2) SAUDI(-3) C stat. P-value PTA (12) LM test (12) Notes: Numbers in italic represent P-values. VAR system has been estimated with 3 lags according to Akaike information selection criterion, C represents a constant in the VAR system. PTA represents Residual Portmanteau Tests for Autocorrelations up to lag 12. LM represents Residual Serial Correlation LM Tests up to lag

211 Table 5-18 Variance Decomposition for the Forecast Error of Daily Market Returns for GCC Markets and Oil Return During the First Sub-Period By innovations in Market Horizon All explained (days) BAHRAIN OIL OMAN KUWAIT ABUDHABI SAUDI foreign* BAHRAIN OIL OMAN KUWAIT ABUDHABI SAUDI Entries in each cell are the percentage of forecast error variance of the market return in the first column explained by the market in the first row * Entries in the 'All foreign' column denote that the total percentage of forecast error variance of the market in the first column explained by all foreign markets Cholesky Ordering: BAHRAIN OMAN KUWAIT ABUDHABI SAUDI OIL Standard Errors: Monte Carlo (1000 repetitions) Table 5-19 presents the variance decomposition for the second sub-period. After the sharp rise in oil prices, one can find that in general all variables in the system still exogenous. The percentage of the foreign explanatory power is not very strong; it does not exceed 5%; though the degree of influence differs across returns. Table 5-19 shows that oil returns influences Saudi market and account for 46% of the variance in the Saudi market explained by foreigners (1.88% out of 4.12% for 6 days horizon). The reverse is right since the Saudi market accounts for most of the variance in the oil returns explained by foreigners for 6 and 9 days horizon. In addition, Abu Dhabi market accounts for half of the variance of Kuwaiti market explained by foreign markets. The results can not help us to determine which the dominant market is in the system that influences all the others and links their interdependence. 200

212 Table 5-19 Variance Decomposition for the Forecast Error of Daily Market Returns for GCC Markets and Oil Return During the Second Sub-Period By innovations in Market Horizon All explained (days) BAHRAIN OIL OMAN KUWAIT ABUDHABI SAUDI foreign* BAHRAIN OIL OMAN KUWAIT ABUDHABI SAUDI Entries in each cell are the percentage of forecast error variance of the market return in the first column explained by the market in the first row * Entries in the 'All foreign' column denote that the total percentage of forecast error variance of the market in the first column explained by all foreign markets Cholesky Ordering: BAHRAIN OMAN KUWAIT ABUDHABI SAUDI OIL Standard Errors: Monte Carlo (1000 repetitions) - Impulse responses The estimated impulse responses of the VAR system offer an additional way of examining how each of the six variables responds to innovations from other variables in the system. Table 1 through 4 and figure 1 and 2 in appendix 4; summarize the responses of all markets to one standard deviation shock in oil returns and the responses of oil returns to one standard deviation shock in each of the GCC markets for the two subperiods. Table 1 presents the response of all markets returns to one standard deviation shock in oil return for the first sub-period. In general the responses are small starting from day 2 and taper off very slowly indicating that markets are not efficient in responding to shock generating from oil return. However, it is apparent that a shock originated in oil return has a major and persistent impact on Omani market more than other GCC markets. It took Omani market 2 days to start responding to oil return s shock. 201

213 In addition, Kuwaiti and Omani markets respond positively while other markets respond negatively to shock in oil return. Table 2 in appendix 4 presents the response of oil return to shocks generated from each of the GCC stock markets and reveals that, shocks generated from Bahraini and Omani markets have large and persistent impact on oil return. Since oil response has a memory up to 15 days reaching the value of , at the end of 15 days horizon for both Bahraini and Omani markets respectively. However, the response of oil returns taper off quickly and died up to 7 days, for shocks generated from Kuwaiti, Abu Dhabi, and Saudi markets. These results are for the first sub-period, but for the second period which witnessed the sharp raising in oil prices, we have a different picture for the interaction relationship between GCC stock markets and oil return. Table 3 presents the responses of GCC stock markets to a shock generated from oil returns. Saudi market stands to be the most influenced followed by Abu Dhabi market. Saudi market reacts in day 2 through 5. The same reaction for Abu Dhabi and Kuwaiti markets, it seems that these markets react quickly and relatively efficient since their reaction taper off and decline rabidly up to day 5 to shock originated in oil return. However, Bahraini and Omani markets show a small and slow process in responding to oil shock. The impulse responses for shocks originated in the GCC stock markets for the second sub-period and the influence on oil return are presented in table 4 of appendix 4. It seems that a shock in the Saudi market has the most influence on oil return, since oil returns has a memory up to 4 days to absorb shocks generated from Saudi market, In day 2 reaching at the end of day 4, while oil return exhibits slow and persistent process in responding to Omani market s shock, at the end of 15 days. In addition, the least response of oil return seems to be for a shock in Abu Dhabi market. Despite the different magnitude of impulse response values, some observations can be made from the above mentioned tables: For the first sub-period the response of GCC stock markets for a shock in oil return seems to be small and taper off slowly. On the other hand and for the same sub-period, the response of oil to shocks in Bahraini and Omani markets seems to be large and persistent. 202

214 For the second sub-period and after oil prices get higher, the interaction between oil return and GCC stock markets increased especially for Saudi, Abu Dhabi, and Kuwaiti markets, they exhibit large and quick responses to oil shocks within 4 days horizon. The reverse also is true when oil responds to shocks generated from these markets. Indicating that, the interaction process between these three markets and oil appears to be efficient. While for Bahraini and Omani markets which are Non- OPEC members, they interact slowly for oil shocks. The Saudi market exerts the great effect when oil prices get higher in the second sub-period. This is must not be surprised, since Saudi Arabia is the largest oil exporter and has the largest oil reserves in the world. These findings reflect the important impact of the increase in oil prices on GCC stock markets. This is quite natural since GCC countries produce about 21% of the world's daily oil production, and they possess about 43% of the world's oil reserve. 5-4 summary This chapter has examined the degree to which Arab stock markets are integrated both regionally and globally. According to the fact that Arabian economies are not similar, Arab stock markets have been divided into two groups: oil production countries which mainly include GCC markets, and non-oil production countries (Jordan, Egypt, and Palestine). The results indicated that Arab stock markets appear to be segmented from international stock markets, since no cointegrating relation was found between Arabian and international markets, represented by S&P 500, (VAR-10). Moreover, evidence of regional financial integration between Arab stock markets is still weak, since the results from VAR-9 and its VEC model indicate that, despite the existence of longrun relationship, linkages on the short run still weak between these markets, even it is difficult to identify the true leader between the nine Arab stock markets. As for GCC markets, the results of VAR-6; which includes 6 GCC markets, indicate that Kuwait market can be considered as a leader for these markets. In addition, some directional relationships have been found between GCC indices, but one still expects to find more short-run relations between countries, share many economic and social aspects. The results for non-oil countries indicate that these markets are not 203

215 cointegrated in the long-run (VAR-3). Moreover, the chapter also examined the effect of the raise in oil prices in the last two years on GCC stock markets, through investigating the dynamic structure between five member countries of the oil-rich GCC and oil prices. The results show that oil prices dominate the long-run equilibrium with GCC stock markets (VAR-7), and have a significant effect on returns volatility in these markets. More important, the findings reflect the important impact of the increase in oil prices on GCC stock markets, since after the rise in oil prices, four out five GCC markets can predict oil prices while only two GCC markets can be predicted by oil prices. The results on the link between oil prices and stock markets are consistent with the existing international literature, since it is found that oil prices shocks have significant effect on stock markets (see Johnes and Kaul 1996; Huang et al. 1996; Sadorsky 1999; Papapetrou 2001; and Hammoudeh and Aleisa 2002, 2004). For portfolio diversification benefits, the message of these results for international investors appears clear. Arab stock markets can offer diversification potentials for international investors; both on the long and short-run horizons; since the body of evidence in support of integration of major world stock markets is quite impressive, international investors often search for new emerging markets which offer the riskreward trade-offs, they cannot get in more matured markets. The results here suggest that Arab stock markets may have such potential benefits; stocks in these markets can minimize the risk of spillovers from other foreign markets (like the US market), and thus may limit the contagion effects which inflect more globally integrated markets. The apparent segmentation of Arab stock markets suggests that these markets are not only emerging, with enormous growing potentials, but they also offer international investors diversification benefits unavailable elsewhere. For regional portfolio investments, non-oil countries (Jordan, Egypt, and Palestine) could offer the rich GCC investors with diversification benefits to diversify their portfolio regionally. From a policy point of view, Arab policy makers should make regulatory and accounting changes to promote financial integration among their markets especially for GCC countries. They should allow more eligible companies to their own to be listed on their exchanges, and permit cross-company listing. Privatization and privately held companies will spread the risk and lead to greater market development. Furthermore, the 204

216 results for GCC markets suggest that there is a bidirectional relationship between Saudi market and oil prices, and a directional relation from Oman to oil prices, the things that point to those countries predictive power on oil prices. This would also show that, political and economic stability (or lack thereof), has a direct impact on stability in oil prices. Saudi Arabia, for example, which has the largest oil reserve in the world (about 24%), does affect oil prices and simultaneously be affected by them. Equally important is that; decision makers in those countries have to secure diverse income resources, and try to increase their contribution (non-oil sectors) to GDP. Since Oman, for instance, oil contribution to GDP amounts to about 42%, Kuwait 46.6%, and Saudi Arabia 38%, in This may lead to risks, as a result of linking those countries economies with oil prices in view of the risks in the oil market, which reflects negatively on the performance and volatility of their stock markets; taking into account that GCC countries are planning to perform a single currency before

217 6- Vision and strategic plan for Arab stock markets The empirical results indicate that the weak form of efficient market hypothesis can be rejected in the Arab stock markets, and that these markets are characterized with thin trading and non-linear return generating process. Despite the noticeable developments in the performance of these markets, as designated by markets performance and activity indicators, these markets are still suffering from numerous problems and obstacles that detain their development and growth, such problems are: the limitation of the investment opportunities available for investors, caused by tiny number of listed companies, highly concentrated markets since few number of listed companies possess most of trading activity, and fewness of the institutional investors since most of the existing investors are individual ones, the fact that these individual investors are less informed and lacked of investment awareness in general which lead them to act in a manner similar to noise trading, which negatively affect the stability and performance of Arab stock markets. In addition, such behavior may lead returns to respond non-linearly to the arrival of new information to the market. On the contrary, the institutional investor makes his decision based on scientific analysis of the available information, a fact that leads to market stability and reduce sharp prices fluctuations. Moreover, the empirical results indicate that Arab stock markets are not integrated with international markets or among themselves. Since no signs of cointegration were found between oil and non-oil production countries stock markets and between Arab markets as a group and international markets. The thing that may provide primary indicators that Arab stock markets could offer diversification potentials for both regional and international portfolio investors. But the fact that, GCC stock markets impose restrictions on both foreign and non-gcc national Arab investors practically diminish these diversification potentials. Additionally, the results present the increasing risks resulting from direct linkages between GCC economies and oil prices, and its important effect on the performance of the GCC-stock markets. It is the time that GCC countries should take considerable steps toward diversifying their GDP components, to decrease their dependence on oil sector. For instance, it is expected that oil reserve in Oman to be consumed within 18 years with 206

218 the current level of production, while oil contribution to GDP account for 42 percent, for Saudi Arabia 38 percent, and Kuwait 42 percent, the thing that clarifies the risks caused by high significant correlation and dependence between these economies and oil production. As a result, political and economic stability (or lack thereof) of the largest oil producers, also has implications for the stability of oil prices. Since Saudi Arabia has the largest oil reserve and production in the world. Moreover, the GCC countries exist in a region characterized with political instability and witnessed three regional wars during the last two decades. 6-1 Environment analysis To understand the causes and consequences of the obtained results, and its implications on Arab stock markets, it is important to analyze the surrounding environment where these markets exist. It can be concluded that among the problems that these markets suffer and affect there performance and efficiency are thin trading, narrowness and illiquidity, the fewness of the available investment opportunities, and the segmentation from international and regional stock markets. The remaining of this section will analyze the environment surrounding these markets and determine the shortages. The thing that lead us to identify and determine the strength, weakness, opportunities and threats that face Arab stock markets, as a way to draw a general strategic plan to develop and enhance the performance of these markets Demand and supply of financial papers In addition to the legal and institutional framework adequacy and completeness, and the availability of efficient executive management, the development of the stock market depends on the availability of sufficient demand and supply. The supply level in Arab stock markets still weak, leading these markets to be narrow, and affects negatively market liquidity and trading activities. Since one can find hundreds of listed companies in one market, without any effective role on trading floor (i.e. Egyptian stock market). Where listing for prestige and reputation purposes will not offer financial papers for the market, it is important to handle and treat this negative phenomenon. 207

219 Financial paper supply could be enhanced by new issues for existing companies, privatizing state owned shares in these companies, the conversion of individual owned companies to corporate ones, in addition to the creation of new financial instruments, will increase and improve investors investment options. To achieve the above, it is needed to remove legal and legislation obstacles, facilitate stock market procedures, allowing for cross-listing between markets, and increase investors confidence with financial intermediaries in these markets. On the other hand, supply policies should be followed with other policies to attract investors attention. It is not expected that supply level still increasing with fixed or slowly demand growth. Since weak demand level will be reflected with low level of coverage for the new issues, which discourage companies to issue new issues. So it is important for Arabian decision makers to implement polices that motivate the demand of financial papers in their local stock markets, keeping supply growth at the same time. The priority of such polices may switch between treating deficiencies in financial papers supply, or increase and motivate financial papers demand, according to each market specific features and conditions. For instance, GCC markets need to concentrate on improving supply to absorb surplus liquidity resulting from huge raise in oil prices, and to reduce negative speculations resultant from huge demand on financial papers. There are several policies that can be followed to motivate demand, such as increasing investment awareness, especially in Arabian countries who suffer from financial deficit, where commercial banks dominate financing sector through short-run finance. Arab stock markets are required to increase investment awareness with stock market s activities and investment opportunities that a stock market can offer. Moreover, it is important to develop the specialized financial press, and create new financial and investment instruments to achieve investors needs to increase the demand on financial papers and improve stock market activities. Arab stock markets need to change the current situation since trading took place only on bonds and common stocks. They have to create new instruments and papers that mixed between bonds and stocks; such as warrants and options, which will improve demand and supply on financial papers. Furthermore, trading costs affect the demand and stock market microstructure, which will be discussed in details later. In addition, to the policies that aimed to develop 208

220 supply and demand, different policies could be used that affect both demand and supply, such as improving the efficiency of intermediaries operations, increasing information, and market control Market microstructure The particular trading arrangements in an equity market may directly affect two key functions of that country s stock market: price discovery and liquidity. First, the trading process should lead to fair and correct prices; in other words, no investor should be able to manipulate market prices in his or her favor. Second, trading should occur at a, low transaction cost, and large quantities should trade without affecting the price. These issues are the topic of the field of market microstructure. It is generally believed that microstructure improvements should greatly affect the liquidity of the markets, which can best be approximated by the cost of trading and increasing the turnover and liquidity in the market. While in the case of Arab stock markets, trading costs (market commissions and intermediaries costs) are significantly affect investors enthusiasm to deal with financial market. That is why Arab markets not only need to reduce trading costs as possible, but also to harmonize and unify these costs among markets, since it is noticeable the existence of large Bid-Ask spread in these markets. Moreover, the differences between trading costs make it difficult to discover the opening treading price, and affect negatively market liquidity Liberalization and markets integration Market integration is central to both questions. In finance, markets are considered integrated when assets of identical risk command the same expected return irrespective of their domicile. In theory, liberalization should bring about emerging market integration with the global capital market, and its effect on emerging markets is then clear. Foreign investors will bid up the prices of local stocks with diversification potential while all investors will shun inefficient sectors. Overall, the cost of equity capital should go down, which in term may increase investment and ultimately increase economic welfare. 209

221 Moreover, market integration is of concern both to equity investors, and companies in the region that make capital budgeting decisions. Specifically, if segmentation exists and firm is forced to raise capital locally, then its cost of capital is likely to be higher than that of a company with unrestricted access to the regional and international capital markets. The empirical results here indicate that Arab stock markets are segmented from international stock markets. Segmentation also exists between oil and non-oil Arabian countries. The thing that raise the needs for these markets to take significant steps toward market liberalization, especially for GCC markets, which impose several restrictions on foreign investors and in some cases even for Arabian investors who are non-gcc nationals. On the other hand, increases in correlations between markets would imply a decrease in the benefits from international diversification in line with portfolio theory. However, under the concept of cost-benefit analysis, Arab stock markets will gain more benefits from market liberalization. Since the tendency for the global markets to become more integrated; is a result of the increasing tendency toward liberalization and deregulation in the money and capital markets, both in developed and developing countries as well as on a bilateral and multilateral basis. Such liberalization is important to introduce structural reforms, to promote economic efficiency, to stimulate trade and investment, and to create a necessary climate for promoting sustainable economic growth with a commitment to market-based reforms Privatization In most emerging markets, privatization was intended to increase productivity of state-owned economic enterprises (SOEs), and to help reduce government budget deficits. In some cases, governments actively sought to promote capital market development through privatization. Many governments intended to create a class of people with a stake in the new economy, thereby making it more difficult for political changes to be reversed. Regardless of the goal, privatization was not initiated, in order to divest fully the government s interest in the real economy. Nevertheless, even the partial divestment under consideration was economically substantial. 210

222 Privatization programs impact emerging capital markets through various mechanisms. For instance, share issued privatizations (SIPs) increase the market capitalization and the value traded on local exchanges. Moreover, SIPs can change the investment opportunity set of portfolio investors, public offers of SOEs whose cash flows are not perfectly correlated with pre-existing companies; help investors to achieve gains through diversification. Under this scenario, SIPs may help to lower the risk premium investors require for holding the market portfolio of publicly traded equity. Other methods of privatization, including the direct sale of former SOEs, the direct sale of SOEs assets, or concessions of public sector monopolies, alter the dynamics of local capital markets in less obvious ways. Consider the direct sale of an SOE to a private investor, this sale does not increase the market capitalization or value traded of the local exchange. However, the sale may alter the real investment opportunity set of the private investor. As viewed from this perspective, all forms of privatization can impact local capital market dynamics. The common component of privatization that impacts capital markets is the transfer of productive resources from the public sector to the private sector. This transfer may allow investors to achieve benefits through diversification and may affect the cost of capital in emerging markets. Even if private investors do not benefit from the transfer of resources, i.e. their investment opportunity set does not change; privatization programs may still influence capital markets. Privatization program can help the government signal its commitment to free market polices. For most emerging markets governments, the implementation of a privatization program reverses decades of state-led economic development. Successful privatization of politically sensitive industries may convince investors to reduce the ex ante perceived risk of government interference in investment decisions and expropriation of productive assets. As a result of sustained privatization efforts, the sovereign risk premium inherent in the governments fixed income liabilities may be reduced. As this chain of events ripples through the economy, local market entrepreneurs eventually benefit in their ability to obtain debt financing at lower cost. Despite the fact that most Arabian countries going toward privatization programs of state-owned economic enterprises, investment opportunities are still limited, investors 211

223 still facing a few number of listing companies with limited financial instruments. It is important for such countries to continue with an effective privatization process to enhance market depth, liquidity, and trading activities Legal and regulatory environment Regulatory framework is a combination of laws, legislations, and instructions that manage financial papers issuing and trading. In addition to the companies and financial papers laws; which are the main components of the regulatory and legal framework, there are several legislations related to financial papers such as investment and taxation laws, auditing law, and banks and financial institutions laws. These legislation, instructions and decisions produced by market supervision party, are forming the fundamental structure that a stock market build on, determining the types and nature of financial instruments, identifying listing requirements and conditions, disclosures standards, and brokerage rules. Moreover, these laws controlling trading process, deposits and clearing settlements, and professional behavior and ethics. - Companies laws Companies law is the most important law that related to stock market. It manages the establishment and registration procedures for corporate companies, and determines the types of financial papers that companies can issue, issuing conditions and requirements, and many other features that manage and protect owners and investors rights, such as those related to financial disclosure and controlling requirements. So companies law directly affects the supply of financial papers. In this consequence, companies laws in several Arabian countries have been reviewed and subjected to full comprehensive revision, mainly aimed to improve companies establishments conditions, and enhancing the procedures that manage financial papers issuing. - Financial papers law and its related instructions Financial papers law is the main step for establishing and organizing a stock market. Despite the differences between financial papers laws among Arab stock markets, they are generally similar in several aspects such as stock market establishing procedures, regulatory framework, and its goals and objectives. 212

224 Despite the regulatory and legislation improvements in most Arab markets, there are still several shortages and disadvantages, most of them may be resulted from the inadequate, inefficient implementations of the contents of these laws, which will be summarized as follows: Some regulatory and financial papers laws have been created in early stages, according to theoretical framework without practical examination. This put these laws disable to follow continuing improvements in international stock markets. Moreover, continues adjustments for some of these laws articles make them inconsistent and unstable over time. A disadvantage that face these markets that they are controlled by several laws and regulations governed by numerous parties. The absence of regulations in some Arabian markets that separate between the supervision and the executive role. The two roles continue to be simultaneously in the hands of the capital market it self (as in Bahrain, Saudi Arabia, Kuwait, and Oman). The absence of legislations in some Arabian markets that forced listed companies to be committed with international accounting and auditing standards, since some of financial statements lack of the adequate disclosure, ambiguity, and incomparability. Despite the existence of legislation that oblige listed companies to issue mid and quarterly financial statements, these statements in several cases came out too late which make them useless. The lake of legislations that manage the establishments of issuing houses. Since such houses promote and insure the full coverage of new issues, and play a significant role in privatization programs especially when it is difficult for a stock market to absorb a large number of new issues. The shortage of legislations that regulate the establishments of credit rating and issuing credit status institutions. In many cases, the lack of regulations that manage and allow cross-listing between Arabian markets, where in other cases; especially GCC markets, 213

225 accessibility of foreign investments is restricted, even for non-gcc nationals Arab investors. The lack of coordination and harmonization between regulatory and legal frameworks among Arab stock markets, which limit the integration ability among these markets, especially for GCC countries which are planning to establish a monetary union and have a single currency before Strength, Weakness, Opportunities, and Threats (SWOT) analysis According to the previous analysis, and taking into account each market special features, characteristics, and environment, it is possible in general to determine the most significant strength, weakness, threats, and opportunities that Arab stock markets enjoy as follow: - Strength According to international standards, Arab stock markets are considered as emerging markets with promising growth potentials. The segmentation of Arab stock markets from international markets protects them from the contagion effect caused by international financial crisis, and raises the diversification potentials that these markets may offer. - Weakness Arab stock markets can be characterized as inefficient markets in the sense of weak-form of efficient market hypothesis. These markets are described as narrow markets and lack of depth. Thin trading and illiquidity. The narrowness of the available investment opportunities with little number of listed companies and limited investment instruments. The lake of investment awareness in general between investors. The diminish role of institutional investor, since most investors are small and individual ones. Highly concentrated markets, since a small portion of listed companies dominate most of trading activities. 214

226 The unavailability of timely, adequate, and reliable financial information about listed companies. The weak; and in some cases the absence, of the financial intermediaries role and the lack of specialized financial information institutions. Several shortages in the legal and regulatory framework and in many cases the inadequate implementation of these laws and legislations. The absence of effective coordination and cooperation among Arab stock markets. Lack of harmonization and highly trading costs among these markets. The existence of many obstacles that hinder the direct flow of foreign investment especially in GCC stock markets. - Opportunities The benefits and advantages that may be obtained from market liberalization process. The available opportunities of growth, since Arab stock markets are emerging markets. The financing potentials that these markets can offer for local and regional companies who are looking for raising funds with low cost of capital. The contributions of Arab stock markets in developing local economies and facilitate privatization programs, through offering several investment channels, encouraging saving, promoting investment, and efficient capital allocation. Attracting foreign investors through offering diversification potentials, after removing all obstacles that prevent direct foreign investments. Offering profitable investment opportunities for expatriate Arab investments, and encourage the return of these investments which are estimated with billions of dollars. - Threats The direct linkages between GCC economies and oil industry, which increase risks in its financial markets and affected by sharp fluctuations in oil prices. 215

227 The risk of sharp and extensive speculations that affect GCC stock markets resultant from surplus liquidity and the narrowness of investment options available for investors. Table 6-1: Strength, Weakness, Opportunities, and Threats for Arab Stock Markets STRENGTH BASED ON According to international standards, Arab stock markets are Emerging markets have promising considered as emerging markets with promising growth potentials. growth potentials. The segmentation of Arab stock markets from international Results of cointegration test, Structural markets protects them from the contagion effect caused by VAR. international financial crisis, and raises the diversification potentials that these markets may offer. WEAKNESS BASED ON Arab stock markets can be characterized as inefficient markets in Regression analysis, Variance ratio, the sense of weak-form of efficient market hypothesis. Runs test, Serial correlation, BDS test, and Volatility analysis GARCH models, Existing anomalies. These markets are described as narrow markets and lack of depth. The lack of several investment opportunities and instruments. Thin trading and illiquidity. Large Bid-Ask spread, see section The narrowness of the available investment opportunities with little Few number of listed companies, limited number of listed companies and limited investment instruments. investment instruments, see table The lake of investment awareness in general between investors. Large number of unwell informed individual and small investors. The diminish role of institutional investor, since most investors are Large waves of speculations, non-linear small and individual ones. returns generating process, BDS test Highly concentrated markets, since a small portion of listed See figure 2-5 companies dominate most of trading activities. The unavailability of timely, adequate, and reliable financial The delay in issuing mid and quarterly information about listed companies. financial statements. The weak; and in some cases the absence, of the financial Lack of investments awareness, intermediaries role and the lack of specialized financial information unavailability of specialized financial institutions. press Several shortages in the legal and regulatory framework and in Inaccessibility of direct foreign many cases the inadequate implementation of these laws and investments to GCC markets. legislations. 216

228 The absence of effective coordination and cooperation among Arab stock markets. Lack of harmonization and highly trading costs among these markets. The existence of many obstacles that hinder the direct flow of foreign investment especially in GCC stock markets. Non-GCC national Arab investors are forbidden to directly invest in GCC markets. Several differences between trading costs among Arab stock markets. Inaccessibility of direct foreign investments to GCC markets, even for non-gcc nationals Arab investors OPPORTUNITIES The benefits and advantages that may be obtained from market liberalization process. The financing potentials that these markets can offer for local and regional companies who are looking for raising funds with low cost of capital The contributions of Arab stock markets in developing local economies and facilitate privatization programs, through offering several investment channels, encouraging saving, and promote investment. Attracting foreign investors through offering diversification potentials, after removing all obstacles that prevent direct foreign investments Offering profitable investment opportunities for expatriate Arab investments, and encourage the return of these investments which are estimated with billions of dollars. BASED ON Entrance of foreign investments, improving market liquidity, depth, & activities. Cointegration test, SVAR results. The effective role that stock market can play in efficient capital allocation, absorbing new issues. The segmentation between these markets and international financial markets, cointegration test, and SVAR The availability of profitable investment options THREATS The direct linkages between GCC economies and oil industry, which increase risks in its financial markets and affected by sharp fluctuations in oil prices. The risk of sharp and extensive speculations that affect GCC stock markets resultant from surplus liquidity and the narrowness of investment options available for investors. BASED ON Cointegration test and VAR analysis for oil prices and GCC markets, the significant role of oil prices on GCC market volatility GARCH models. High waves of speculations, weak supply with growing demand on financial papers. 217

229 6-3 Vision and strategic goals The objective of this action plan is therefore to achieve the following strategic goals, which expected to enhance and develop Arab stock markets, increasing market liberalization, improving market efficiency, through increasing market liquidity, depth, and trading activities. The thing that put these markets in a position that able to attract portfolio investors, signs these markets as a target from foreign investors, and facilitates the integration and cointegration between Arab and international and regional financial markets. To achieve these goals, it was necessary to draw specific targets under the umbrella of a general vision, which will be achieved through specific strategies that could be realized through tactical programs and activities, as described bellow. Taking into account the following couple of notes: The following programs are not exclusive, any other additional programs and activities could be added, to achieve the goals. It is important to consider the specific conditions for each individual market, regarding economic factors, regulatory frameworks, and the liberalization degree that a stock market reached. This strategic plan aims to achieve several objects in order to reach two main goals: the first goal aims to enhance and improve market efficiency through enhancing the legal and regulatory frameworks that manage these markets, improving market microstructure, increasing market depth and liquidity, the thing that increasing the capability to liberalize and open Arab stock markets with international markets, which is the second main goal. - Vision for Arab stock markets: Toward liberalized, liquid and efficient Arab stock markets, with the capability to attract foreign investments and portfolios by providing several investment instruments and opportunities, that offer diversification potentials. 218

230 - Strategic plan The Main Goals: Goal 1: Increasing and improving Arab stock markets efficiency. Goal 2: Liberalizing Arab stock markets. Goal 1: Increasing and improving Arab stock markets efficiency Target 1: Increasing market depth and liquidity by enhancing trading activities. Strategy 1: Increasing financial papers supply and diversify the available investment opportunities and instruments. Adopting the required legal procedures that encourage companies for listing in stock markets. Promoting cross-listing for financial papers among Arab markets Increasing tax incentives for listing companies. Offering new financial instruments through amending the related legislations and adequate implementation of regulations. Reviewing and reducing trading costs as possible. Strategy 2: Increasing and enhancing demand on financial papers. Increasing investment awareness in general. Creating investment funds to attract the less informed small investors. Providing frequent, qualified financial information. Increasing and activating the role of financial intermediaries institutions. Target 2: Protecting investors rights and keep on market stability away from sharp fluctuations. Strategy 1: The availability of adequate financial information regarding listed companies activities and performance. 219

231 Forcing listed companies with international accounting and auditing standards especially those related to full financial disclosure Creating specialized financial press. Encouraging the existence of expert and credit rating houses. Forcing listing companies to issue regulatory financial statements on its time especially semi & quarterly statements. Strategy 2: Improving market microstructure. Reducing trading costs as possible. Full isolation and clear determination of responsibilities between the supervision & the executive functions according to the international stock markets standards. Strategy 3: Increasing and enhancing the role of institutional investor. Goal 2: Liberalizing Arab stock markets Target 1: Harmonization and conformance of regulations and legislations among Arab stock markets. Strategy 1: Relaxation of the legal and regulatory impediments. Allowing non-gcc notional investors to directly invest in GCC stock markets. Strategy 2: Increasing correlation and integration between Arab stock markets. Unification of listed conditions & commissions. Unification of clearing & deposits regulations among Arab stock markets. Eliminating the legal obstacles that hinder cross listing between markets. Harmonizing tax laws among Arab countries especially those related to financial markets. Improving & renewing companies law. Enhancing financial papers law. Target 2: Facilitating the mobility of international and regional portfolios especially to GCC markets. Strategy 1: Attracting international and regional investments and portfolios. 220

232 Removing all obstacles that prevent foreign direct investments in GCC markets. Strategy 2: Emphasizing the diversification benefits that Arab stock markets may offer for portfolio investors. Providing reliable financial information for listed companies activities and market performance. Improving & introducing new trading techniques such as E. trading. Target 3: Increasing Arab markets openness to international and regional markets. Strategy 1: Benefiting from advantages and experiences resultant from integration with international markets. Increasing investment and financing options available for investors. Encouraging the establishments of institutions specialized in producing financial information which promote Arab markets internationally. Removing any obstacles that prevent foreign direct investment mobility to GCC markets. Target 4: Enhancing the role of financial market in economic development and efficient capital allocation. Strategy 1: Create investment tools and opportunities to encourage saving. Establishing investment funds targeted small investors, i.e. pension funds. Offering several saving tools and opportunities. Encourage the return of Arab expatriate investments through offering profitable investment options. Absorbing the surplus liquidity resultant from raise in oil prices. Strategy 2: offering different financing sources for those companies looking for raising fund. Absorbing the new companies issues via effective and deep stock market. Offering several financing options for companies. 221

233 Strategy 3: contribution in achieving governments privatization programs and diversified GDP components in GCC countries to reduce oil risks. Absorbing the state-owned enterprises (SOEs) issues resulting from privatization programs. The availability of issuing houses those secure the full coverage of the new issues. 222

234 Goal 1: Increasing and improving Arab stock markets efficiency Strategic Goal 1 Increasing and improving Arab stock markets efficiency Specific Objects Increasing market depth and liquidity by enhancing trading activity Protecting investors rights and keep on market stability away from sharp fluctuations Strategies Increasing financial papers supply and diversify the available investment opportunities and instruments Increasing and enhancing the demand for financial papers The availability of adequate financial information regarding listed companies activities and performance Improving market microstructur e Increasing & enhancing the role of institutional investor - Adopting the required legal procedures that encourage companies for listing in stock markets - Promoting cross-listing for financial papers among Arab markets - Increasing tax incentives for listing companies - Offering new financial instruments through amending the related legislations and adequate implementation of regulations - Reviewing and reducing trading costs as possible - Increasing investment awareness in general - Creating investment funds to attract the less informed small investors - Providing frequent, qualified financial information - Increasing and activating the role of financial intermediaries institutions - Forcing listed companies with international accounting and auditing standards especially those related to full financial disclosure - Creating specialized financial press - Encouraging the existence of expert and credit rating houses - Forcing listing companies to issue regulatory financial statements on its time especially semi & quarterly statements - Reducing trading costs as possible - Full isolation and clear determination of responsibilities between the supervision & the executive functions according to the international stock markets standards 223

235 Goal 2: Liberalizing Arab stock markets Strategic Goal 2 Arab stock markets liberalization Specific Objects Harmonization and conformance of regulations & legislations among Arab stock markets Facilitating the mobility of international & regional portfolios especially to GCC markets Integrating & correlating Arab markets with international markets Enhancing the role of the financial market in economic development & efficient capital allocation Relaxation of the legal & regulatory impediments Increasing correlation & integration between Arab markets Attracting international & regional investments & portfolios Emphasizing the diversification benefits that Arab markets may offer for portfolio investors Benefiting from advantages & experiences resultant from integration with international markets Create investment tools and opportunities to encourage saving, Offering different financing sources for those companies looking for raising fund Contribution in achieving governments privatization programs and diversified GDP components in GCC countries to reduce oil risks - Allowing non-gcc notional investors to directly invest in GCC stock markets - Unification of listed conditions & commissions - Unification of clearing & deposits regulations among Arab stock markets - Eliminating the legal obstacles that hinder cross listing between markets - Harmonizing tax laws among Arab countries especially those related to financial markets - Improving & renewing companies law - Enhancing financial papers law - Removing all obstacles that prevent foreign direct investments in GCC markets - Providing reliable financial information for listed companies activities & market performance - Improving & introducing new trading techniques such as E. trading - Increasing investment and financing options available for investors - Encouraging the establishments of institutions specialized producing financial information which promote Arab markets internationally - removing any obstacles that prevent foreign direct investment mobility to GCC markets - Establishing investment funds targeted small investors, i.e. pension funds - Offering several saving tools and opportunities - Encourage the return of Arab expatriate investments through offering profitable investment options - Absorbing the surplus liquidity resultant from raise in oil prices - Absorbing the new companies issues via effective & deep stock market - Offering several financing options for companies - Absorbing the SOIs issues resulting from privatization program - The availability of issuing houses those secure the full coverage of the new issues 224

236 7- Conclusions The Efficient Market Hypothesis (EMH) states that asset prices in financial markets should reflect all available information. As a consequence, prices should always be consistent with fundamentals. Most studies on EMH were conducted on the world s largest stock markets. However, the past twenty years have witnessed spectacular growth in size and relative importance of emerging markets in developing countries. Emerging markets have long posed a challenge for finance; standard models are often ill suited to deal with the specific circumstances arising in these markets. However, the interest in emerging markets has provided impetus for both the adaptation of current models to new circumstances in these markets and the development of new models. The focus of this thesis was toward nine Arab new emerging markets in the Middle East region. Having presented the main literature regarding EMH, our attention has been directed to test market efficiency in these markets. Consider the specific features for these markets; we started firstly by adjusting daily observed indices for the possible effect of infrequent trading. Based on the results obtained from chapter 4, random walk properties have been rejected for Arab stock markets. The results obtained from regression analysis, variance ratio, BDS, runs test, and serial correlation tests, rejected the randomness and independence of returns, even after observed indices have been corrected for infrequent trading. Moreover, the results indicated that, prices responding nonlinearly to the arrival of new information, while volatility clustering phenomenon still seems to characterize markets returns. The GARCH (1,1) results for daily returns indicated that all markets exhibited volatility clustering with one exception for Dubai. Furthermore, volatility seems to be persistent in three markets (Egypt, Kuwait, and Palestine) with a slow rate of decay. Additionally, four Arab markets (Bahrain, Dubai, Kuwait, and Oman) showed signs of leverage effect with asymmetric shocks to volatility. The GARCH models found to explain quite satisfactory the non-linear dependence found in the time series. Furthermore, seasonality and calendar effects existed in Arab markets with three forms; day-of-the-week effect, month-of-the-year effect and the Halloween indicator. Having answered the question regarding market efficiency in Arab stock markets, we tried to examine the degree to which these markets are integrated with other international markets. In other words, we tried to investigate if Arab stock markets can offer diversification 225

237 benefits for both regional and international portfolios investors. The analysis has been conducted in two directions, firstly to investigate the cointegrating relation between Arab and international markets, while the second direction was to examine the dynamic relationships between Arab markets as a group. The results of multivariate cointegration techniques indicated that Arab stock markets appear to be segmented from international markets, since no cointegrating relation was found among variables in the system. However, in the short-run there could be some interactions between Arab and international markets. To examine this, a structural vector autoregression (SVAR) model has been employed, to answer the question how do Arab markets react to shocks originated in international markets (US, UK, and Japan) under the assumption that, the returns on each of the three international markets affect the returns on Arab markets but not vise versa. The resultant impulse response functions and variance decomposition indicated that, the linkages between Arab and international markets still very weak in the short-run, with some signs that the UK market exerted the most effect in influencing Arab markets. Next we continued to examine the dynamic relationships between Arab markets themselves. According to the fact that Arabian economies are not similar, the total markets have been divided into two groups: oil production countries which mainly contain GCC markets, and non-oil production countries (Jordan, Egypt, and Palestine). The results indicated that despite the existing long-run cointegrating relation, the linkages between Arab markets still weak in the short run, while for non-oil countries indicated that these markets are not integrated on the long-run. It is known that several economic factors may affect stock market performance; such as oil prices, given the fact that oil prices have a significant effect on GCC economies. We continue chapter 5 by investigating the effect of oil prices on GCC stock markets. Several techniques have been used, firstly to test the effect of oil prices on market volatility, oil returns have been added as an additional regressor in the variance equation of the GARCH model, the results indicated that oil prices have a significant role in affecting GCC markets volatility. Second, using multivariate cointegration and vector autoregression (VAR) models, we concluded that oil prices formed and dominated the long-run cointegration with GCC markets. Furthermore, after the raise in oil prices; especially during the last two years, the linkages between oil and GCC markets increased, four GCC markets have predictive power on oil prices, with only two markets (Saudi Arabia and Oman) to be predicted by oil prices. Finally, and on the light of the obtained empirical results, a strategic plan has been suggested to develop the performance of Arab stock markets based on two main broad goals, improving market efficiency and increasing market liberalization. This will be 226

238 achieved through specific targets and strategies that could be realized through tactical programs and activities. In conclusion, based on the results of this thesis; some implications for economic policy can be made. For portfolio diversification benefits, the message of these results for international investors appears clear, Arab stock markets can offer diversification potentials for international investors; both on the long and short-run horizons. The results here suggested that Arab stock markets may have diversification potentials; stocks in these markets can minimize the risk of spillovers from other foreign markets (like the US market), and thus may limit the contagion effects which inflect more globally integrated markets. The apparent segmentation of Arab stock markets suggested that these markets are not only emerging, with enormous growing potentials, but they also offer international investors diversification benefits unavailable elsewhere. For regional portfolio investments, non-oil countries (Jordan, Egypt, and Palestine) could offer the rich GCC investors with diversification benefits to diversify their portfolio regionally. From a policy point of view, Arab policy makers should make necessary relaxation to regulatory and legal framework, to promote financial integration among their markets especially for GCC countries. They should allow more eligible companies to their own to be listed on their exchanges, and permit cross-company listing. Privatization and privately held companies will spread the risk and lead to greater market development. Moreover, the results for GCC markets suggest that there is a bidirectional relationship between Saudi market and oil prices, and a directional relation from Oman to oil prices, the things that point to those countries predictive power on oil prices. This would also show that, political and economic stability (or lack thereof), has a direct impact on stability in oil prices. Saudi Arabia, for example, which has the largest oil reserve in the world (about 24%), does affect oil prices and simultaneously be affected by them. Equally important is that; decision makers in these countries have to secure diverse income resources, and try to increase the contribution of non-oil sectors to GDP. Since Oman, for instance, oil contribution to GDP amounts to about 42%, Kuwait 46.6%, and Saudi Arabia 38%, in This may lead to risks, as a result of linking these countries economies with oil prices in view of the risks in the oil market, which reflected 227

239 negatively on the performance and volatility of their stock markets; taking into account that GCC countries are planning to perform a single currency before

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257 Appendixes 246

258 Appendix 1 Graph Plots for Each Arab Stock Market Index for both Levels and Returns on Daily Basis. A: Indices Logarithm Kuwait AbuDhabi Bahrain Saudi Dubai Oman Palestine Jordan Egypt

259 continue appendix 1 B: Indices Returns Kuwait AbuDhabi Bahrain Saudi Dubai Oman Palestine Jordan Egypt

260 Appendix 2 Diagnostic Tools for both Random Walk and GARCH Models Table 1 Diagnostic Tools for Reseduals of the RW Model R t =α+ε t for the Observed Indices Abudhabi Bahrain Dubai BDS test BDS test BDS test m ε =0.5 ε =1 ε =2 m ε =0.5 ε =1 ε =2 m ε =0.5 ε =1 ε = McLeod-Li test lags Obs*R 2 Prob. McLeod-Li test lags Obs*R 2 Prob. McLeod-Li test lags Obs*R 2 Prob LM test lags Obs*R 2 Prob. LM test lags Obs*R 2 Prob. LM test lags Obs*R 2 Prob ARCH test lags Obs*R 2 Prob. ARCH test lags Obs*R 2 Prob. ARCH test lags Obs*R 2 Prob Serial correl. lags Q-stat. Prob. Serial correl. lags Q-stat. Prob. Serial correl. lags Q-stat. Prob Normality test J.B Prob. Normality test J.B Prob. Normality test J.B Prob SC SC SC

261 continue table 1 Egypt Jordan Kuwait BDS test BDS test BDS test m ε =0.5 ε =1 ε =2 m ε =0.5 ε =1 ε =2 m ε =0.5 ε =1 ε = McLeod-Li test lags Obs*R 2 Prob. McLeod-Li test lags Obs*R 2 Prob. McLeod-Li test lags Obs*R 2 Prob LM test lags Obs*R 2 Prob. LM test lags Obs*R 2 Prob. LM test lags Obs*R 2 Prob ARCH test lags Obs*R 2 Prob. ARCH test lags Obs*R 2 Prob. ARCH test lags Obs*R 2 Prob Serial correl. lags Q-stat. Prob. Serial correl. lags Q-stat. Prob. Serial correl. lags Q-stat. Prob Normality test J.B Prob. Normality test J.B Prob. Normality test J.B Prob SC SC SC

262 continue table 1 Oman Palestine Saudi BDS test BDS test BDS test m ε =0.5 ε =1 ε =2 m ε =0.5 ε =1 ε =2 m ε =0.5 ε =1 ε = McLeod-Li test lags Obs*R 2 Prob. McLeod-Li test lags Obs*R 2 Prob. McLeod-Li test lags Obs*R 2 Prob LM test lags Obs*R 2 Prob. LM test lags Obs*R 2 Prob. LM test lags Obs*R 2 Prob ARCH test lags Obs*R 2 Prob. ARCH test lags Obs*R 2 Prob. ARCH test lags Obs*R 2 Prob Serial correl. lags Q-stat. Prob. Serial correl. lags Q-stat. Prob. Serial correl. lags Q-stat. Prob Normality test J.B Prob. Normality test J.B Prob. Normality test J.B Prob SC SC SC Notes:The reseduals of the RW model for the observed indices are under investigation in this part. The BDS test results were not altered and only P-values are reported.lm test stands for Breusch- Godfrey LM test statistic with associated P -values and tests for serial correlations in the reseduals.arch test is a lagrange multiplier (LM) test for autoregressive conditional heteroskedasticity(arch) in the residuals proposed by Engle Q -stattistic is the Ljung-Box Q -statistics and their P -values, The Q -statistic at lag K is a test statistic for the null hyputhesis that there is no autocorrelation up to order K. J.B is Jarque-Bera statistic for testing normality, SC is the Schwartz criterion for random walk model. 251

263 Table 2 Diagnostic Tools for Standarised Reseduals of the GARCH(1,1) Model for Daily Returns of the Observed Indices Abudhabi Bahrain Dubai BDS test BDS test BDS test m ε =0.5 ε =1 ε =2 m ε =0.5 ε =1 ε =2 m ε =0.5 ε =1 ε = McLeod-Li test lags Q-stat. Prob. McLeod-Li test lags Q-stat. Prob. McLeod-Li test lags Q-stat. Prob ARCH test lags Obs*R 2 Prob. ARCH test lags Obs*R 2 Prob. ARCH test lags Obs*R 2 Prob Serial correl. lags Q-stat. Prob. Serial correl. lags Q-stat. Prob. Serial correl. lags Q-stat. Prob Normality test J.B Prob. Normality test J.B Prob. Normality test J.B Prob E SC SC SC

264 continue table 2 Egypt Jordan Kuwait BDS test BDS test BDS test m ε =0.5 ε =1 ε =2 m ε =0.5 ε =1 ε =2 m ε =0.5 ε =1 ε = McLeod-Li test lags Q-stat. Prob. McLeod-Li test lags Q-stat. Prob. McLeod-Li test lags Q-stat. Prob ARCH test lags Obs*R 2 Prob. ARCH test lags Obs*R 2 Prob. ARCH test lags Obs*R 2 Prob Serial correl. lags Q-stat. Prob. Serial correl. lags Q-stat. Prob. Serial correl. lags Q-stat. Prob Normality test J.B Prob. Normality test J.B Prob. Normality test J.B Prob SC SC SC

265 continue table 2 Oman Palestine Saudi BDS test BDS test BDS test m ε =0.5 ε =1 ε =2 m ε =0.5 ε =1 ε =2 m ε =0.5 ε =1 ε = McLeod-Li test lags Q-stat. Prob. McLeod-Li test lags Q-stat. Prob. McLeod-Li test lags Q-stat. Prob ARCH test lags Obs*R 2 Prob. ARCH test lags Obs*R 2 Prob. ARCH test lags Obs*R 2 Prob Serial correl. lags Q-stat. Prob. Serial correl. lags Q-stat. Prob. Serial correl. lags Q-stat. Prob Normality test J.B Prob. Normality test J.B Prob. Normality test J.B Prob SC SC SC Notes:The reseduals of the GARCH(1,1) model for the observed indices with oil returns as a regressor are under investigation in this part. The BDS test results were not altered and only P-values are reported.mcleod-li test examen the serial correlation of the squered reseduals.arch test is a lagrange multiplier (LM) test for autoregressive conditional heteroskedasticity(arch) in the residuals proposed by Engle Q -stattistic is the Ljung-Box Q -statistics and their P -values, The Q -statistic at lag K is a test statistic for the null hyputhesis that there is no autocorrelation up to order K in the standarized resedual. J.B is Jarque-Bera statistic for testing normality of the standarized reseduals, SC is Schwartz criterion for GARCH model. 254

266 Table 3 Diagnostic Tools for Reseduals of the EGARCH(1,1) Model for Daily Returns of the Observed Indices Abudhabi Bahrain Dubai BDS test BDS test BDS test m ε =0.5 ε =1 ε =2 m ε =0.5 ε =1 ε =2 m ε =0.5 ε =1 ε = ARCH test lags Obs*R 2 Prob. ARCH test lags Obs*R 2 Prob. ARCH test lags Obs*R 2 Prob Serial correl. lags Q-stat. Prob. Serial correl. lags Q-stat. Prob. Serial correl. lags Q-stat. Prob Normality test J.B Prob. Normality test J.B Prob. Normality test J.B Prob E SC SC SC

267 continue table 3 Egypt Jordan Kuwait BDS test BDS test BDS test m ε =0.5 ε =1 ε =2 m ε =0.5 ε =1 ε =2 m ε =0.5 ε =1 ε = ARCH test lags Obs*R 2 Prob. ARCH test lags Obs*R 2 Prob. ARCH test lags Obs*R 2 Prob Serial correl. lags Q-stat. Prob. Serial correl. lags Q-stat. Prob. Serial correl. lags Q-stat. Prob Normality test J.B Prob. Normality test J.B Prob. Normality test J.B Prob

268 continue table 3 Oman Palestine Saudi BDS test BDS test BDS test m ε =0.5 ε =1 ε =2 m ε =0.5 ε =1 ε =2 m ε =0.5 ε =1 ε = ARCH test lags Obs*R 2 Prob. ARCH test lags Obs*R 2 Prob. ARCH test lags Obs*R 2 Prob Serial correl. lags Q-stat. Prob. Serial correl. lags Q-stat. Prob. Serial correl. lags Q-stat. Prob Normality test J.B Prob. Normality test J.B Prob. Normality test J.B Prob SC SC SC Notes:The standarised reseduals of the EGARCH(1,1) model for the observed indices are under investigation in this part. The BDS test results were not altered and only P-values are reported.lm test stands for Breusch-Godfrey LM test statistic with associated P -values and tests for serial correlations in the reseduals.arch test is a lagrange multiplier (LM) test for autoregressive conditional heteroskedasticity(arch) in the residuals proposed by Engle Q -stattistic is the Ljung-Box Q -statistics and their P -values, The Q -statistic at lag K is a test statistic for the null hyputhesis that there is no autocorrelation up to order K. J.B is Jarque-Bera statistic for testing normality. 257

269 Table 4 Diagnostic Tools for Standarised Reseduals of the GARCH(1,1) Model for Daily Returns of GCC Markets with Oil Prices Abudhabi Bahrain Dubai BDS test BDS test BDS test m ε =0.5 ε =1 ε =2 m ε =0.5 ε =1 ε =2 m ε =0.5 ε =1 ε = McLeod-Li test lags Q-stat. Prob. McLeod-Li test lags Q-stat. Prob. McLeod-Li test lags Q-stat. Prob ARCH test lags Obs*R 2 Prob. ARCH test lags Obs*R 2 Prob. ARCH test lags Obs*R 2 Prob Serial correl. lags Q-stat. Prob. Serial correl. lags Q-stat. Prob. Serial correl. lags Q-stat. Prob Normality test J.B Prob. Normality test J.B Prob. Normality test J.B Prob E

270 conyinue table 4 Oman Kuwait Saudi BDS test BDS test BDS test m ε =0.5 ε =1 ε =2 m ε =0.5 ε =1 ε =2 m ε =0.5 ε =1 ε = McLeod-Li test lags Q-stat. Prob. McLeod-Li test lags Q-stat. Prob. McLeod-Li test lags Q-stat. Prob ARCH test lags Obs*R 2 Prob. ARCH test lags Obs*R 2 Prob. ARCH test lags Obs*R 2 Prob Serial correl. lags Q-stat. Prob. Serial correl. lags Q-stat. Prob. Serial correl. lags Q-stat. Prob Normality test J.B Prob. Normality test J.B Prob. Normality test J.B Prob Notes:The reseduals of the GARCH(1,1) model for the observed indices with oil returns as a regressor are under investigation in this part. The BDS test results were not altered and only P-values are reported.mcleod-li test examen the serial correlation of the squered reseduals.arch test is a lagrange multiplier (LM) test for autoregressive conditional heteroskedasticity(arch) in the residuals proposed by Engle Q -stattistic is the Ljung-Box Q -statistics and their P -values, The Q -statistic at lag K is a test statistic for the null hyputhesis that there is no autocorrelation up to order K in the standarized resedual. J.B is Jarque-Bera statistic for testing normality of the standarized reseduals. 259

271 Table 5 DiagnosticTools for reseduals from RW Model R t adj =α+ε t for the Corrected Indices Abudhabi Bahrain Dubai BDS test BDS test BDS test m ε =0.5 ε =1 ε =2 m ε =0.5 ε =1 ε =2 m ε =0.5 ε =1 ε = McLeod-Li test lags Obs*R 2 Prob. McLeod-Li test lags Obs*R 2 Prob. McLeod-Li test lags Obs*R 2 Prob LM test lags Obs*R 2 Prob. LM test lags Obs*R 2 Prob. LM test lags Obs*R 2 Prob ARCH test lags Obs*R 2 Prob. ARCH test lags Obs*R 2 Prob. ARCH test lags Obs*R 2 Prob Serial correl. lags Q-stat. Prob. Serial correl. lags Q-stat. Prob. Serial correl. lags Q-stat. Prob Normality test J.B Prob. Normality test J.B Prob. Normality test J.B Prob E

272 continue table 5 Egypt Jordan Kuwait BDS test BDS test BDS test m ε =0.5 ε =1 ε =2 m ε =0.5 ε =1 ε =2 m ε =0.5 ε =1 ε = McLeod-Li test lags Obs*R 2 Prob. McLeod-Li test lags Obs*R 2 Prob. McLeod-Li test lags Obs*R 2 Prob LM test lags Obs*R 2 Prob. LM test lags Obs*R 2 Prob. LM test lags Obs*R 2 Prob ARCH test lags Obs*R 2 Prob. ARCH test lags Obs*R 2 Prob. ARCH test lags Obs*R 2 Prob Serial correl. lags Q-stat. Prob. Serial correl. lags Q-stat. Prob. Serial correl. lags Q-stat. Prob Normality test J.B Prob. Normality test J.B Prob. Normality test J.B Prob

273 continue table 5 Oman Palestine Saudi BDS test BDS test BDS test m ε =0.5 ε =1 ε =2 m ε =0.5 ε =1 ε =2 m ε =0.5 ε =1 ε = McLeod-Li test lags Obs*R 2 Prob. McLeod-Li test lags Obs*R 2 Prob. McLeod-Li test lags Obs*R 2 Prob LM test lags Obs*R 2 Prob. LM test lags Obs*R 2 Prob. LM test lags Obs*R 2 Prob ARCH test lags Obs*R 2 Prob. ARCH test lags Obs*R 2 Prob. ARCH test lags Obs*R 2 Prob Serial correl. lags Q-stat. Prob. Serial correl. lags Q-stat. Prob. Serial correl. lags Q-stat. Prob Normality test J.B Prob. Normality test J.B Prob. Normality test J.B Prob Notes:The reseduals of the RW model for the corrected indices are under investigation in this part. The BDS test results were not altered and only P-values are reported.lm test stands for Breusch-Godfrey LM test statistic with associated P -values and tests for serial correlations in the reseduals.arch test is a lagrange multiplier (LM) test for autoregressive conditional heteroskedasticity(arch) in the residuals proposed by Engle Q -stattistic is the Ljung-Box Q -statistics and their P -values, The Q -statistic at lag K is a test statistic for the null hyputhesis that there is no autocorrelation up to order K. J.B is Jarque-Bera statistic for testing normality. 262

274 Table 6 Diagnostic Tools for Reseduals from Non-linear model for Observed Indices R t = a 0 + a 1 R t-1 + a 2 R 2 t-1+ a 3 R 3 t-1+ ε t Abudhabi Bahrain Dubai BDS test BDS test BDS test m ε =0.5 ε =1 ε =2 m ε =0.5 ε =1 ε =2 m ε =0.5 ε =1 ε = McLeod-Li test lags Obs*R 2 Prob. McLeod-Li test lags Obs*R 2 Prob. McLeod-Li test lags Obs*R 2 Prob LM test lags Obs*R 2 Prob. LM test lags Obs*R 2 Prob. LM test lags Obs*R 2 Prob ARCH test lags Obs*R 2 Prob. ARCH test lags Obs*R 2 Prob. ARCH test lags Obs*R 2 Prob Serial correl. lags Q-stat. Prob. Serial correl. lags Q-stat. Prob. Serial correl. lags Q-stat. Prob Normality test J.B Prob. Normality test J.B Prob. Normality test J.B Prob E

275 continue table 6 Egypt Jordan Kuwait BDS test BDS test BDS test m ε =0.5 ε =1 ε =2 m ε =0.5 ε =1 ε =2 m ε =0.5 ε =1 ε = McLeod-Li test lags Obs*R 2 Prob. McLeod-Li test lags Obs*R 2 Prob. McLeod-Li test lags Obs*R 2 Prob LM test lags Obs*R 2 Prob. LM test lags Obs*R 2 Prob. LM test lags Obs*R 2 Prob ARCH test lags Obs*R 2 Prob. ARCH test lags Obs*R 2 Prob. ARCH test lags Obs*R 2 Prob Serial correl. lags Q-stat. Prob. Serial correl. lags Q-stat. Prob. Serial correl. lags Q-stat. Prob Normality test J.B Prob. Normality test J.B Prob. Normality test J.B Prob

276 continue table 6 Oman Palestine Saudi BDS test BDS test BDS test m ε =0.5 ε =1 ε =2 m ε =0.5 ε =1 ε =2 m ε =0.5 ε =1 ε = McLeod-Li test lags Obs*R 2 Prob. McLeod-Li test lags Obs*R 2 Prob. McLeod-Li test lags Obs*R 2 Prob LM test lags Obs*R 2 Prob. LM test lags Obs*R 2 Prob. LM test lags Obs*R 2 Prob ARCH test lags Obs*R 2 Prob. ARCH test lags Obs*R 2 Prob. ARCH test lags Obs*R 2 Prob Serial correl. lags Q-stat. Prob. Serial correl. lags Q-stat. Prob. Serial correl. lags Q-stat. Prob Normality test J.B Prob. Normality test J.B Prob. Normality test J.B Prob Notes:The reseduals of the non-linear model for the observed indices are under investigation in this part. The BDS test results were not altered and only P- values are reported.lm test stands for Breusch-Godfrey LM test statistic with associated P -values and tests for serial correlations in the reseduals.arch test is a lagrange multiplier (LM) test for autoregressive conditional heteroskedasticity(arch) in the residuals proposed by Engle Q -stattistic is the Ljung- Box Q -statistics and their P -values, The Q -statistic at lag K is a test statistic for the null hyputhesis that there is no autocorrelation up to order K. J.B is Jarque-Bera statistic for testing normality. 265

277 Table 7 Diagnostic Tools for Reseduals from Non-linear Model for Corrected Indices R adj t = a 0 + a 1 R adj t-1 + a 2 R 2adj t-1+ a 3 R 3adj t-1+ ε t Abudhabi Bahrain Dubai BDS test BDS test BDS test m ε =0.5 ε =1 ε =2 m ε =0.5 ε =1 ε =2 m ε =0.5 ε =1 ε = McLeod-Li test lags Obs*R 2 Prob. McLeod-Li test lags Obs*R 2 Prob. McLeod-Li test lags Obs*R 2 Prob LM test lags Obs*R 2 Prob. LM test lags Obs*R 2 Prob. LM test lags Obs*R 2 Prob ARCH test lags Obs*R 2 Prob. ARCH test lags Obs*R 2 Prob. ARCH test lags Obs*R 2 Prob Serial correl. lags Q-stat. Prob. Serial correl. lags Q-stat. Prob. Serial correl. lags Q-stat. Prob Normality test J.B Prob. Normality test J.B Prob. Normality test J.B Prob E

278 continue table 7 Egypt Jordan Kuwait BDS test BDS test BDS test m ε =0.5 ε =1 ε =2 m ε =0.5 ε =1 ε =2 m ε =0.5 ε =1 ε = McLeod-Li test lags Obs*R 2 Prob. McLeod-Li test lags Obs*R 2 Prob. McLeod-Li test lags Obs*R 2 Prob LM test lags Obs*R 2 Prob. LM test lags Obs*R 2 Prob. LM test lags Obs*R 2 Prob ARCH test lags Obs*R 2 Prob. ARCH test lags Obs*R 2 Prob. ARCH test lags Obs*R 2 Prob Serial correl. lags Q-stat. Prob. Serial correl. lags Q-stat. Prob. Serial correl. lags Q-stat. Prob Normality test J.B Prob. Normality test J.B Prob. Normality test J.B Prob

279 continue table 7 Oman Palestine Saudi BDS test BDS test BDS test m ε =0.5 ε =1 ε =2 m ε =0.5 ε =1 ε =2 m ε =0.5 ε =1 ε = McLeod-Li test lags Obs*R 2 Prob. McLeod-Li test lags Obs*R 2 Prob. McLeod-Li test lags Obs*R 2 Prob LM test lags Obs*R 2 Prob. LM test lags Obs*R 2 Prob. LM test lags Obs*R 2 Prob ARCH test lags Obs*R 2 Prob. ARCH test lags Obs*R 2 Prob. ARCH test lags Obs*R 2 Prob Serial correl. lags Q-stat. Prob. Serial correl. lags Q-stat. Prob. Serial correl. lags Q-stat. Prob Normality test J.B Prob. Normality test J.B Prob. Normality test J.B Prob Notes:The reseduals of the non-linear model for the corrected indices are under investigation in this part. The BDS test results were not altered and only P- values are reported.lm test stands for Breusch-Godfrey LM test statistic with associated P -values and tests for serial correlations in the reseduals.arch test is a lagrange multiplier (LM) test for autoregressive conditional heteroskedasticity(arch) in the residuals proposed by Engle Q -stattistic is the Ljung- Box Q -statistics and their P -values, The Q -statistic at lag K is a test statistic for the null hyputhesis that there is no autocorrelation up to order K. J.B is Jarque-Bera statistic for testing normality. 268

280 Figure 1 News Impact Curve: Volatility σ2, Against the Impact Ζ = ε / σ where: log 2 2 ( σ ) ˆ ˆ log( 1) ˆ ˆ t = ω + β σ t + a Ζt 1 + γζt 1 SIG news impact curve Egypt SIG news impact curve Jordan SIG news impact curve Palestine Volatility 8 6 news impact curve Dubai Z Z Z news impact curve Abudhabi news impact curve Kuwait news impact curve Oman news impact curve Saudi SIG SIG SIG SIG Z Z Z Z news impact curve Israel news imoact curve Japan news impact curve UK news impact curve Bahrain SIG SIG SIG SIG Z Z Z Z news impact curve Turkey news impact curve USA news impact curve India SIG SIG SIG Z Z 269

281 Appendix 3 Table 1 Variance Decomposition of Forecast Error of Daily Market Returns for Arab Stock Markets to Structural Inovvations in International Markets Market Horizon explained (days) USA UK Japan Bahrain Oman Kuwait Saudi Dubai AbuDhabi Egypt Palestine Jordan Entries in each cell are the percentage of forcast error variance of the market return in the first column explained by the market in the first row. Factorization: Structural Decomposition. By innovations in 270

282 Table 2 Impulse Response of a Unit Shock Generated by International Markets on Arab Stock Markets Bahrain Oman Kuwait Period USA UK Japan USA UK Japan USA UK Japan Dubai AbuDhabi Egypt Period USA UK Japan USA UK Japan USA UK Japan Jordan Saudi Palestine Period USA UK Japan USA UK Japan USA UK Japan Factorization: Structural Decomposition. Standard Errors: Monte Carlo (1000 repetitions). 271

283 Figure 1 Response of AbuDhabi market from 1 July 2001 to 31 December 2003 Response of Dubai market from 26 March 2000 to 31 December 2003 Response of the Jordanian market from 1 January 1992 to 14 March Accumulated Response of ABUDHABI to Structural One S.D. Shock in Japan Accumulated Response of DUBAI to Structural One S.D. Shock in Japan.004 Accumulated Response of JORDAN to Structural One S.D. Shock in Japan Accumulated Response of ABUDHABI to Structural One S.D. Shock in UK Accumulated Response of ABUDHABI to Structural One S.D. Shock in USA Accumulated Response of DUBAI to Structural One S.D. Shock in UK Accumulated Response of DUBAI to Structural One S.D. Shock in USA Accumulated Response of JORDAN to Structural One S.D. Shock in UK Accumulated Response of JORDAN to Structural One S.D. Shock in USA

284 continue figure Response of the Omani market from 1 February 1997 to 13 October 2004 Accumulated Response of OMAN to Structural One S.D. Shock Japan Response of the Saudi market from 26 January 1994 to 14 March 2005 Accumulated Response of SAUDI to Structural One S.D. Shock in Japan Response of the Palestinian market from 8 July 1997 to 28 February Accumulated Response of PALESTINE to Structural One S.D. Shock in Japan Accumulated Response of OMAN to Structural One S.D. Shock in UK -.12 Accumulated Response of SAUDI to Structural One S.D. Shock in UK.00 Accumulated Response of PALESTINE to Structural One S.D. Shock in UK Accumulated Response of OMAN to Structural One S.D. Shock in USA -.09 Accumulated Response of SAUDI to Structural One S.D. Shock in USA.08 Accumulated Response of PALESTINE to Structural One S.D. Shock in USA

285 continue figure 1 Response of the Bahraini market from 1 January 1991 to 3 June Accumulated Response of BAHRAIN to Structural One S.D. Shock in Japan Response of the Egyptian market from 1 January 1998 to 31 December Accumulated Response of EGYPT to Structural One S.D. Shock in Japan Response of the Kuwaiti market from 17 January 2001 to 9 March Accumulated Response of KUWAIT to Structural One S.D. Shock in Japan Accumulated Response of BAHRAIN to Structural One S.D. Shock in UK Accumulated Response of EGYPT to Structural One S.D. Shock in UK Accumulated Response of KUWAIT to Structural One S.D. Shock in UK Accumulated Response of BAHRAIN to Structural One S.D. Shock in USA Accumulated Response of EGYPT to Structural One S.D. Shocki in USA Accumulated Response of KUWAIT to Structural One S.D. Shock in USA

286 Appendix 4 Table 1 Accumulated Response of All Markets to One S.D Oil Innovation for the First Sub-Period Period BAHRAIN OIL OMAN KUWAIT ABUDHABI SAUDI Table 2 Accumulated Response of Oil to One S.D Innovation in GCC Markets for the First Sub-Period Period BAHRAIN OMAN KUWAIT ABUDHABI SAUDI

287 Table 3 Accumulated Response of All Markets to One S.D Oil Innovation for the Second Sub-Period Period BAHRAIN OIL OMAN KUWAIT ABUDHABI SAUDI Table 4 Accumulated Response of Oil to One S.D Innovation in other Markets for the Second Sub-Period Period BAHRAIN OMAN KUWAIT ABUDHABI SAUDI

288 Figure 1 Accumulated Response of all markets to One S.D. Innovations ± 2 S.E in oil for the first sub-period. Response of OMAN to OIL Response of KUWAIT to OIL Response of ABUDHABI to OIL Response of SAUDI to OIL Response of BAHRAIN to OIL Response of OIL to OIL Accumulated Response of oil to One S.D. Innovations ± 2 S.E.in all markets for the first sub-period Response of OIL to BAHRAIN.8.8 Response of OIL to OMAN Response of OIL to KUWAIT.8 Response of OIL to ABUDHABI.8.8 Response of OIL to SAUDI

289 Figure 2 Accumulated Response of all markets to One S.D. Innovations ± 2 S.E.in oil for the second sub-period Response of OMAN to OIL Response of KUWAIT to OIL Response of ABUDHABI to OIL Response of SAUDI to OIL Response of BAHRAIN to OIL Response of OIL to OIL Accumulated Response of oil to One S.D. Innovations ± 2 S.E.in all markets for the second sub-period Response of OIL to BAHRAIN.8.8 Response of OIL to OMAN Response of OIL to KUWAIT.8 Response of OIL to ABUDHABI.8.8 Response of OIL to SAUDI

290 Appendix 5: Middle East Map Including Arabian Countries under Examination. A rab ian cou ntries under exam ination 279