Comparative Study of Volatility Forecasting Models: The Case of Malaysia, Indonesia, Hong Kong and Japan Stock Markets

Abstract

This paper aims to investigate the effectiveness of four volatility forecasting models, i.e. Exponential Weighted Moving Average (EWMA), Autoregressive Integrated Moving Average (ARIMA) and Generalized Auto-Regressive Conditional Heteroscedastic (GARCH), in four stock markets Indonesia, Malaysia, Japan and Hong Kong. Using monthly closing stock index prices collected from 1st January 1998 to 31st December 2015 for the four selected countries, results obtained confirm that volatility in developed markets is not necessarily always lower than the volatility in emerging markets. Among all the three models, GARCH (1, 1) model is found to be the best forecasting model for stock markets in Malaysia, Indonesia, and Japan, while EWMA model is found to be the best forecasting model for Hong Kong stock market. The outperformance of GARCH (1, 1) found supports again what is found in Minkah (2007).