Modeling and Forecasting Volatility of the Malaysian and the Singaporean Stock Indices using Asymmetric GARCH Models and Non-normal Densities

This paper examines and estimates the three garch(1,1) models (garch, egarch and gjr-garch) using daily price data. two asian stock indices klci and sti were studied using daily data over a 14-years period. the competing models include garch, egarch and gjr-garch using the gaussian normal, student-t and generalized error distributions. the estimates showed that the forecasting performance of asymmetric garch models (gjr-garch and egarch), especially when fattailed densities are taken into account in the conditional volatility, are better than symmetric garch. moreover, it was found that the ar(1)-gjr model provides the best out-of-sample forecast for the malaysian stock market, while ar(1)-egarch provides a better estimation for the singaporean stock market