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 bashar38@yahoo.com. 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