Evaluating the Forecasting Performance of Symmetric and Asymmetric GARCH Models across Stock Markets

Abstract

Recently, the stock market volatility has created a surge among the researchers to focus their attention towards studying the sensitivity of stock market returns. In this study, the method of OLS has been applied to study the sensitivity of stock market returns to macroeconomic fundamentals. The performance of OLS (Ordinary Least Square Method) has not been BLUE (Best Linear Unbiased Estimator) due to the existence of heteroskedasticity. The presence of heteroskedasticity is confirmed by the ARCH LM test of Heteroskedasticity. Therefore, Symmetric and Asymmetric GARCH models have been employed to investigate the interaction between the stock market volatility and macroeconomic fundamentals volatility. Apart from this, the forecasting performance of symmetric and asymmetric GARCH models are compared and ranked based on the error measurement approaches such as Mean Squared Error, Root mean squared error and Mean Absolute Percentage Error. The results of the Mean Absolute Percentage Error reveals that the asymmetric E-GARCH model is the superior model to other GARCH models namely TGARCH and symmetric GARCH models in explaining the stock market returns in USA and in UK. Subsequently, the GARCH models outperform well in the US stock market comparing with the UK stock market.