International Research Journal of Finance and Economics ISSN 1450-2887 Issue 85 (2012) © EuroJournals Publishing, Inc. 2012 http://www.internationalresearchjournaloffinanceandeconomics.com Modelling Stock Returns Volatility: Empirical Evidence from Saudi Stock Exchange Suliman Zakaria Suliman Abdalla Assistant Professor (Financial Econometrics) Department of Quantitative Analysis College of Business Administration, King Saud University (Riyadh), Kingdom of Saudi Arabia E-mail: sulabdalla@ksu.edu.sa; Sulimanzakaria9@yahoo.com Tel.:+966560847037; Fax: +9664678648 Abstract This paper aims to model stock return volatility in the Saudi stock market by using daily closing prices on the general market index (Tadawul All Share Index; TASI) over the period of 1 st January 2007 to 26 th November 2011. The paper employs different univariate specifications of the generalized autoregressive conditional heteroscedastic (GARCH) model, including both symmetric and asymmetric models. An application of the GARCH (1,1) model provides strong evidence of the persistence of time varying volatility. By allowing the mean equation of the returns series to depend on a function of the conditional variance, the results provide evidence of the existence of a positive risk premium, which supports the positive correlation hypothesis between volatility and the expected stock returns. Furthermore, the asymmetric GARCH models show a significant evidence for asymmetry in stock returns, confirming the presence of leverage effect in the returns series. Keywords: Saudi Stock Market, Returns Volatility, GARCH Models, Volatility Clustering, Leverage Effects. 1. Introduction In recent years, modeling and analyzing volatility 1 of stock market returns has become an important task in financial markets; it has been extensively researched and received considerable attention from market participants, policy makers and almost all those have something to do with the financial markets. This extensive research reflects the importance of volatility in investment, security valuation, risk management, and monetary policy making (Poon and Granger, 2003). It is widely recognized among both practitioners and academics that volatility is not directly observable and that financial returns volatility exhibits certain characteristics that are specific to financial time series such as volatility clustering 2 and leverage effect 3 (Bollerslev, 1986). To capture these characteristics, financial econometricians have developed a variety of time-varying volatility 1 In general terms, volatility refers to the fluctuations observed in some phenomenon over time. In terms of modelling and forecasting literature, it means “the conditional variance of the underlying asset return” (Tsay, 2010). 2 This mean large changes tend to be followed by large changes and small changes tend to be followed by small changes (Mandelbrot, 1963). 3 This implies that volatility is higher after negative shocks than after positive shocks of the same enormity (Black, 1976).