| Abstract: |
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. |