Modelling and Forecasting the Volatility and Price of Malaysian Stock Market

Modelling and forecasting volatility of a financial time series has been significant area research in recent years, owing to the fact that is regarded as an essential notion many economic applications. Because not directly observable, analysts are especially eager obtain accurate estimation this conditional variance process. As result, number models have developed specifically suited estimate instruments, with most well-known widely used model being heteroscedastic models. The objectives are; forecast price Malaysian stock market; assess performance competing simulate market. This estimates examines ARIMA GARCH family type models, standardized GARCH-M for symmetric EGARCH GJR-GARCH asymmetric using daily return data. For two distributions were which normal distribution t-distribution. market Kuala Lumpur Composite Index (KLCI) was studied data over 11-years period beginning from 1st January 2010 ending on 31st December 2020. results showed method suitable long term since ARCH effect present. While, (GJR-GARCH), when fat-tailed densities taken into account volatility, better than GARCH. In addition, student-t performs distribution. Moreover, it found AR (1) provides best market, KLCI. Thus, concluded coupled distribution, performed well modeling KLCI dataset. process resulted three different outcomes where first one foresees stationary trend RM1,825.00 all years. While second indicates fluctuation around RM1,625.00 last outcome had result shows positive upward RM1,850.00 year 2021.