Evolving Efficiency of Amman Stock Exchange Amjad GH. Alhabashneh. Faculty of Financial and Business Administration, Al al-bayt University, Al.

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1 Evolving Efficiency of Amman Stock Exchange Amjad GH. Alhabashneh Faculty of Financial and Business Administration, Al al-bayt University, Al Mafraq Abstract This paper evolving efficiency of ASE. GARCH-M(1,1) approach along with state space timevarying parameters is used for period ASE market show high sensitivity to past shocks and found to be weak-form inefficient, as the efficiency does not improve toward the begging of 2009 and negatively reacts to contemporaneous crises. Keywords: GARC-M(1,1),Kalman Filter, Evolving Efficiency, Amman Stock Exchange. 1. Introduction Central to investors and policy maker's dealing with emerging equity markets is knowledge of how efficiently those markets incorporate market information into security price. The analysis of the efficient market has attracted a great deal of interest in recent time. The efficient market hypothesis is based on the assumption that at any given time, prices of stocks fully reflect all the available information related to them. Evolving efficiency of ASE provide an excellent case study of emerging stock markets for several reasons. ASE has experienced rapid growth in recent years. The opening of ASE market to foreign investors in 1990s has provided new opportunities for diversification Azzam(2002). Further, the risk-return relationships of stocks listed on ASE are quite unusual, if not anomalous; they exhibit low returns and low volatility. The main objective of this paper is to evolving efficiency of Amman Stock Exchange (ASE). For this purpose, the study will apply GARCH-M (1,1) (Generalized Autoregressive Conditionally Heteroskedastic in Mean) model as well as a Kalman filter state-space in estimating the timevarying dependency of daily returns on their lagged values from and allowing the COPY RIGHT 2015 Institute of Interdisciplinary Business Research 36

2 variance of the error term to vary over time, in contrast with classical regressions assuming constant variance. This paper is organized as follows. Section 2 presents the literature review on evolving efficient market. Data & Methodology presented in section 3. An overview of ASE and estimation are presented in section 4. Section 5 concludes. 2. Literature Review There are many previous studies dealing with the efficiency of emerging stock markets. Through these studies, Khoory and Shinik (1993) study the behavior of stock price in Amman Stock Exchange through selecting 16 companies registered in the stock exchange; the conclusion was that the market did not take a random walk due to strict ties among the changing serials of stock price. As result, the inefficiency of ASE is revealed, where stock price in this market did not completely reflect all available information, according to assumption of weak markets.zalewska et al.(1999),hall and Urga(2002) and Vit Posta(2008),show changing levels of inefficiency in Budapest, China, Russia and Prague stock markets, respectively. Maghyereh (2003) investigate the validity of random walk model for ASE using daily closing price index for period , the results suggest that the behavior of the ASE return series is inconsistent with the random walk model, which implies informationally inefficient. Furthermore Squalli (2005) tested for market efficiency in the selected sectors of the Dubai financial market and Abu Dhabi securities exchange in United Arab Emirates using daily sectoral indices between 2000 and 2005, the results show that insurance sector in Abu Dhabi securities Exchange is weak-form efficient sector. Lagoarde-Segot and Lucey (2008) investigated the efficiency in relation to its theoretical underpinning in asset of seven emerging middle-easter North African stock markets (namely, Egybt, Morocco, Tunisia, Jordan, Lebanon, Israel, and Turkey), the results suggested extent of weak-form efficiency in MENA stock markets primarily explained by differences in stock market size. This paper differs from previous, by studying the dynamics of weak-form inefficiency in emerging stock market (ASE), rather than taking a snap shot of it at a given point of time. Also using daily data of ASE market that covers a wide time period up to 2009, allowing us to test for the impact of the current crises besides those of 2001 and COPY RIGHT 2015 Institute of Interdisciplinary Business Research 37

3 3. Data & Methodology The data used in this study is consisted of daily index time series for ASE from 1 st of January, 01, 1992 to 19 January Additionally, all of the daily index was extracted from electronic database of ASE. Mathematically, we define returns on day t as t r = Ln (p t p t-1 ), where p t is the value of the stock market index at the close of the day. Following Zalewska et al.(1999), Hall and Urga (2002) and Vit Posta(2008), Walid (2010), we use GARCH-M (1,1) (Generalized Autoregressive Conditionally Heteroskedastic in Mean) model as well as a Kalman filter state-space in estimating the time-varying dependency of daily returns on their lagged values and allowing the variance of the error term to vary over time, in contrast with classical regressions assuming constant variance. Also GARCH-M(1,1) allows us to test for the presence of risk premium in the stock markets Elyasiani and Mansure(1998). Our GARCH-M(1,1) is stated as follows: r t = β 0 + β 1t r t-1+ δ h t +e t...(1) e t ~N(0, h t ) h t = h t e 2 t-1.(2) In addition to the intercept (β 0 ) and slope (β 1 ) in equation (1), representing an AR(1) model, δ represents the risk premium parameter in the conditional model, when tradeoff between volatility and return prevails. Returns volatility is measured by conditional variance h t which is described as function of the squared values of the past residuals (e 2 t-1), presenting the ARCH factor, and an auto regressive term (h t-1 ) reflecting the GARCH character of the model. The sum represents the degree of volatility persistence. COPY RIGHT 2015 Institute of Interdisciplinary Business Research 38

4 3.1. Descriptive statistics: The first phase of our analysis is based on descriptive statistics of the returns Table (1): summary statistics of returns. ASE Mean Median Maximum Minimum Std. Dev Skewness Kurtosis Jarque-Bera Probability Source: researcher calculations. Table (1) provides summary statistics, ASE seem to display "stylized" facts common to many financial assets such as non normality in the form of fat tails. The ASE returns fluctuate between and with unconditional mean Second, however in term of risk the unconditional standard deviation of ASE index indicating that extreme return volatility is inherent in ASE, this statistics is consistent with the finding by Maghereh and Alzuobi (2005), as indicated by skewness statistics, Furthermore ASE returns seem to be positively skewed which indicates along right tail in empirical distributions, Leptokurtic behavior is apparent in ASE with fat tails. Also, the Jarque-Bera test which combines the skewness and kurtosis result, indicate that the hypothesis of normality is rejected decisively for ASE return at 5% level. The non normality is apparent from the fatter tails from the normal distribution and positive skewness this nonnormality might be partly due to volatility clustering, if this is true, then removing the volatility clustering should lower the non normality in the series and indicating that some type of GARCH model is appropriate. The analyzed series present a slight right COPY RIGHT 2015 Institute of Interdisciplinary Business Research 39

5 asymmetry therefore, the frequency distributions of returns are no Gaussians but leptokurtic and slightly asymmetric therefore in this case the univariate specification GARCH (1, 1) is very significant to estimate.the Jarque-Bera test strengthens this condition, rejecting the null hypothesis of normality at 5% level for all series. 4. An overview of ASE Market The ASE market has witnessed several improvements in its main indicators over the last years. It serves as good example of newly emerging stock market with significant growth potential. Table (2) shows the main market development statistics during the period Table (2) ASE Market Development Statistics Value traded shares traded market capitalization number of listed company %GDP 2008 %GDP Note value, shares traded and market capitalization in million dollars. The ASE value traded increased from 2598 million $ to million $, share traded increased from 997 million $ to 5112 million $ through period study, so the ASE has succeeded in few years to increase their liquidity as measured by value and share trade. Furthermore, the volume of market capitalization has increase from million $ to million $ through the same period, while the market capitalization to GDP ratio increase from 107% to 184% through the same period. Table (3) shows the estimated GARCH-M (1, 1) models for the ASE. First of all, table 3 shows that β 0 is insignificant for ASE, the dependency of daily returns on their lagged values; β 1 is different from zero, indicating a departure from the weak-form efficiency, the parameter δ, the risk premium parameter, the coefficient is insignificant. Further, GARCH-M(1,1) effects (ARCH COPY RIGHT 2015 Institute of Interdisciplinary Business Research 40

6 and GARCH) are very significant, ASE series show high sensitivity to the past shocks, the measure of volatility persistence given by is very closed to 1 indicating volatility cluster, As the frequency of the data Increases from monthly to daily data the volatility clustering effect will become more important. From this fact it is clear that volatility changes over time. One possible explanation of volatility clustering is that the arrival of new information is serially correlated Engle,R. et al. (1990).Volatility clustering might be caused by the trading on the markets as well When the market is not sufficiently liquid to absorb large trades, this will have quite some effect on volatility. According to Bollerslev, Chou, and Kroner, (1992) volatility clustering means that market tends to be volatile for a week or two and then calms gradually for the following several weeks therefore, if the traders have heterogeneous expectations with some having insider information news may disseminate after one period, However differences in investor expectations may take some time to be eradicated. Volatility clustering also constitutes the transmission of news from one market to another. To capture the volatility clustering, Engle (1982) proposed the class of autoregressive conditional heteroscedastic (ARCH) model has been generalized to the GARCH model by Bollerslev (1986), John et. al. (1997). COPY RIGHT 2015 Institute of Interdisciplinary Business Research 41

7 4.1. GARCH-M (1, 1) estimations Table (3) univariate GARCH (1, 1) estimation results. GARCH-M(1,1) estimation r 1 = r t h t (.8) (0.0) (0.17) h t = h t e 2 t-1 Source: researcher calculations. Notes: the model is r t = β 0 + β 1 r t-1+ δ h t +e t, h t = h t e 2 t-1 Kalman Filter estimations Using the GARCH-M (1,1) specification, the state-space model is formulated in order to take not only changing variance structure in stock returns into consideration but also the time-varying dependency of daily returns on their lagged values as follows: r t = β 0 + β 1t r t-1+ δ h t +e t... (3) e t ~N(0, h t ) h t = h t e 2 t-1..(4) β 1t = β 1t-1 + v it (5) v it ~ N(0, σ 2 i) β 1t, in equation (3), is not to be estimated as constant over time like equation (1), but as a timevarying parameter. Equation (3) being the space or signal equation and equation (4) and (5) the two state equations. Equation (4) describes the behavior of variance of the residuals as before, and equation (5) described the behavior of β 1t following a random walk, Walid (2010). Graph (1) show the time paths of β 1t and 95% confidence interval obtained through Kalman Filter state-space estimation. In ASE, β 1t coefficient departure from zero, this is not consistent with weak-form efficiency properties, since β 1t is significantly different from zero, in spit of temporary improvements in ASE before 2001 and 2006, indeed β 1t move in the wrong direction away from zero during the current crises or since 2006 local crises in Jordan, overall ASE is found weak-form inefficient and inefficiency does not improve towards the first quarter of COPY RIGHT 2015 Institute of Interdisciplinary Business Research 42

8 This reveals the ineffectiveness of reforms undertaken during the last years this is consistent with Walid (2010) find about Arab stock markets. The possible explanation of the weak-form inefficiency of ASE market is that, ASE market characterized by limited numbers of listed companies, participants and involved capital. furthermore, in most emerging markets, problems with liquidity, market fragmentation, absence of official market makers, thin trading and the imposition of price limits are factors that it is argued, contribute to their inefficiency. Seppi (1997), Handaand and Schwartz (1996), Demarchand and Foucault (1998).Furthermore the trading mechanism in ASE suffer from major weakness, lack of immediacy. If for example, there is an imbalance between buy and sell orders during a trading day, successive buy (sell) orders may well get noted on the trading board without counter sell (buy) orders arriving at the market. COPY RIGHT 2015 Institute of Interdisciplinary Business Research 43

9 5. Conclusion ASE market has shown a growing interest for stock markets since the early 1990s, which explains their number and the many reforms undertaken, in order to improve their liquidity and efficiency. The results reveal a clear departure from weak-form efficiency. Overall, efficiency path of ASE market does not show a clear trend towards more efficiency and it is quit instable being affected by the contemporaneous crises. In addition ASE market is highly sensitive to past shocks; also the results reveal the ineffectiveness of the reforms undertaken during the last decade. COPY RIGHT 2015 Institute of Interdisciplinary Business Research 44

10 References: Azzam, H, (2002), There's considerable rationale to invest in Arab stock markets, Middel East Executive Reports 25, 6. Aktham, M. (2003), The Random Walk Hypothesis and the Evidence From the Amman (Jordan) Stock Exchange, Zagreb International Review of Economic & Business, Vol.6, No 1-2,pp Bollerslev, T. (1986). Generalized Autoregressive conditional Heteroscedastic. Journal of Econometrics, (31), Bollerslev, T., Chou, R. and Kroner, K. (1992). ARCH modeling in finance: A review of the theory and empirical evidence. Journal of econometrics, 52, Demirguc, K. and, Huizing, H. (2000), Financial Structure and Bank Profitability, Policy research, Working Paper, The World Bank, mimeo. Engle, R., ltol, T., and Lin, W. (1990). Meteor shavers or heat weaves? Hetroscedastic Intraday volatility in foreign exchange market. Econometrica, 25(3), Engle, R. (1982). Autoregressive conditional Heterosedasticity with Estimates of the variance of United Kingdom inflation, 50, Elyasiani and Mansure. (1998) Sensitivity of the Bank stock Returns Distribution to Changes in the Level and Volatility of Interest Rate: A GARCH-M model, Journal of Banking and Finance, 22, Hall, S. and Urga, G. (2002) Treating for Ongoing Efficiency in the Russian stock Market ttp:\\ Handa, P. and Schwartz, R. (1996a), How best to Supply Liquidity to a securities Market, The journal of Portfolio Management, winter, pp John, y, Andrew, w., and Mackinlay, A. (1997). The Econometrics of financial Markets, U.S, University press Princeton. Khoory and Shink (1993), the behavior of Stock Price in Amman stock Exchange, Yarmook University Researches, Vol.9. Lagoard-segot, T. and Lucey, B., (2008), Stock market predictability in the MENA: Evidence from new variance ration tests and technical trade analysis, IIIS Discussion paper no 92, Trinity College Dublin. COPY RIGHT 2015 Institute of Interdisciplinary Business Research 45

11 Maghyereh, A., and AL-zuob, H. (2005). Free trade agreements and equity market Integration: the case of the US and Jordan. Financial Economics, (15), Seppi,D.(1997), Liquidity Provision With Limit Order and a Strategic: Specialist, Review of Hung RD,Stoll HR, 1996, Dealer versus Auction Markets : a Paird Comparisons Of execution cost on NASDAQ and the NYSE, Journal Of Financial Economics, 41,pp Squalli, j. (2005). Are the UAE Financial Markets Efficient? Zayed University, working Paper, No Vit Posta, (2008), Estimating the Dynamics of Weak Efficiency on the Prague Stock Exchange Using the Kalman Filiter, Czech, Journal of Economics and Finance, 58, no 5-6. Walid, A. (2010), testing the Evolving Efficiency of 11 Arab stock markets, International Review of Financial Analysis, Vol.19, pp Zalewska-Mitura, and Stephen Hall, (1999), Examining the first stages of Market Performance: A test for Evolving market efficiency, Economics Letters, Vol. 64, pp.1-12 COPY RIGHT 2015 Institute of Interdisciplinary Business Research 46