67 Aziz and Iqbal, Journal of International and Global Economic Studies, December 2016, 9(2), 67-78 Comparing Volatility Forecasts of Univariate and Multivariate GARCH Models: Evidence from the Asian Stock Markets Zohaib Aziz * Federal Urdu University of Arts, Sciences and Technology, Karachi-Pakistan Javed Iqbal ** Applied Economics Research Centre, Karachi-Pakistan Abstract: This paper compares the forecasting performance of univariate (EGARCH) and multivariate GARCH models for the volatilities of stock market index returns of Japan, India, Indonesia and Pakistan each paired with the US stock market. We also investigate the role of Global Financial Crisis (GFC) of 2007-2009 in affecting forecasting performance. We investigate whether incorporation of the linkage with the US stock market in a multivariate GARCH framework helps in improving the volatility forecasts of Asian stock markets. The daily stock returns from July 3, 1997 to November 12, 2012 are employed. Forecasts are evaluated using three measures namely, R 2 (coefficient of determination), Mean Absolute Percentage Error (MAPE) and Median Absolute Percentage Error (MdAPE). The results show that correlation with the US helps in improving the accuracy of volatility forecast of Asian stock markets i.e. performance of multivariate GARCH is found to be better than the EGARCH for all the countries considered while including GFC dummy does not result in improved forecast of stock market volatility forecast. Keywords: GARCH, Multivariate, Univariate, Volatility JEL Classification: F37, F47, C58 1. Introduction Stock market volatility plays a prominent role in many financial decision making cases. Stock market volatility is reflected in large stock price movements that often occur in bunch in response to news that are expected to affect firm’s cash flows. Volatility forecasts are employed in several financial activities e.g. in risk management and options pricing. An option trader makes his decsion about the future pay off of the contract through the expected volatility of the underlying asset. Volatility foreecast are also used in hedging, portfolio selection, market making and timing etc (Engle and Patton, 2001). Conditional volatility is an important ingredients in being a part in the computation of important financial measures value-at-risk (VaR), conditional asset pricing and option pricing so obtaining reliable volatility forecast has been an important area of interest for academics and practitioners. The international trade and finance between the economies and deregulation and liberalization of both emerging and developed stock markets have increased the integration of the markets of developed and developing countries in terms of increase in correlation. A question of interest is whether and to what extent this integration improves the volatility forecast of the markets. The US is perhaps the most important financial market and the development in the US financial, economic,