www.ccsenet.org/ijef International Journal of Economics and Finance Vol. 4, No. 3; March 2012 ISSN 1916-971X E-ISSN 1916-9728 216 Modelling Exchange Rate Volatility using GARCH Models: Empirical Evidence from Arab Countries Suliman Zakaria Suliman Abdalla Assistant Professor, Department of Quantitative Analysis, College of Business Administration King Saud University, Riyadh, Kingdom of Saudi Arabia Tel: 96-65-6084-7037 E-mail: sulabdalla@ksu.edu.sa Received: November 24, 2011 Accepted: December 23, 2011 Published: March 1, 2012 doi:10.5539/ijef.v4n3p216 URL: http://dx.doi.org/10.5539/ijef.v4n3p216 Abstract This paper considers the generalized autoregressive conditional heteroscedastic approach in modelling exchange rate volatility in a panel of nineteen of the Arab countries using daily observations over the period of 1 st January 2000 to 19 th November 2011. The paper applies both symmetric and asymmetric models that capture most common stylized facts about exchange rate returns such as volatility clustering and leverage effect. Based on the GARCH(1,1) model, the results show that for ten out of nineteen currencies the sum of the estimated persistent coefficients exceed one, implying that volatility is an explosive process, in contrast, it is quite persistent for seven currencies, a result which is required to have a mean reverting variance process. Furthermore, the asymmetrical EGARCH (1,1) results provide evidence of leverage effect for majority of currencies, indicating that negative shocks imply a higher next period volatility than positive shocks. Finally, the paper concludes that the exchange rates volatility can be adequately modelled by the class of GARCH models. Keywords: Exchange rate volatility, Heteroscedasticity, GARCH model, Volatility clustering, Leverage effect 1. Introduction Over the last few decades, exchange rate movements and fluctuations have become an important subject of macroeconomic analysis and have received a great deal of interest from academics, financial economists and policy makers, particularly after the collapse of the Bretton Woods agreement of fixed exchange rates among major industrial countries. Since then, there has been an extensive debate about the topic of exchange rate volatility and its potential influence on welfare, inflation, international trade and degree of external sector competitiveness of the economy and also its role in security valuation, investment analysis, profitability and risk management. Consequently, a number of models have been developed in empirical finance literature to investigate this volatility across different regions and countries. Well known and frequently applied models to estimate exchange rate volatility are the autoregressive conditional heteroscedastic (ARCH) model advanced by Engle (1982) and generalized (GARCH) model developed independently by Bollerslev (1986) and Taylor (1986). The issue of modelling exchange rate volatility has gained considerable importance in the research studies since 1973, when many countries shifted towards floating exchange rate from fixed exchange rate regime. Part of these studies were conducted to understand the behavior of exchange rate and to explain the sources of its movements and fluctuations. Although, there is no consensus in the literature regarding the factors that influence exchange rate volatility but, generally, it could be explained largely by macroeconomic variables. Many researches indicate some connection between exchange rate volatility and news or information on other macroeconomic fundamentals (including inflation, interest rates, money supply and GDP), see for example, Hartman (1972), De Grauwe (1988), Asseery and Peel (1991), Choi and Prasad (1995), Andersen and Bollerslev (1998), Arize et al. (2000), McKenzie and Faff (2004), Engel and Kenneth (2005), Evans and Lyons (2005), Laakkonen (2007), Lubik and Frank (2007), Mark (2009) and Pavasuthipaisit (2010). On the other hand, many empirical studies have risen significantly in recent years to investigate the characteristics of exchange rate volatility in the context of time series analysis of financial returns such as leverage effect and volatility clustering and persistence. For example, Friedman and Stoddard (1982), Meese and Rogoff (1983), Milhoj (1987), Taylor (1987), Hsieh (1989), Lastrapes (1989), Bollerslev (1990), Pesaran and Robinson (1993), Jorion (1995), McKenzie (1997), Tse and Tsui (1997) Brooks and Burke (1998), Longmore and Robinson (2004), Wang (2006), Yoon and Lee (2008), Hamadu and Adeleke (2009), and Fiser and Roman (2010) find evidence of volatility clustering and persistence which mean that large and small values in the log-returns tend to occur in clusters, and