Modeling and Forecasting Volatility of the Malaysian and the Singaporean stock indices using Asymmetric GARCH models and Non-normal Densities
This paper examines and estimate the three GARCH(1,1) models (GARCH, EGARCH and GJR-GARCH) using the daily price data. Two Asian stock indices KLCI and STI are studied using daily data over a 14-years period. The competing Models include GARCH, EGARCH and GJR-GARCH used with three different distributions, Gaussian normal, Student-t, Generalized Error Distribution. The estimation results show that the forecasting performance of asymmetric GARCH Models (GJR-GARCH and EGARCH), especially when fat-tailed asymmetric densities are taken into account in the conditional volatility, is better than symmetric GARCH. Moreover, its found that the AR(1)-GJR model provide the best out-of- sample forecast for the Malaysian stock market, while AR(1)-EGARCH provide a better estimation for the Singaporean stock market.
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