Modeling and Forecasting the Stock Market Volatility of S&P 500 Index Using GARCH Models

Volatility forecasting is an important area of research in financial markets and immense effort has been expended in improving volatility models, since better forecasts translate themselves into better pricing of options and better risk management. In this direction, the present paper attempts at modelling and forecasting the volatility (conditional variance) of the S&P 500 Index returns of United States stock market, using daily data covering a period from January 1, 1996 to January 29, 2010. The forecasting models that are considered in this study range from the relative simple GARCH (1,1) model to relatively complex GARCH models, including Exponential GARCH (1,1) and Threshold GARCH (1,1) models. Based on out-of-sample forecasts and a majority of evaluation measures, our result shows that the symmetric GARCH model does perform better in forecasting conditional variance of the S&P 500 Index return rather than the asymmetric GARCH models, despite the presence of leverage effect. Our findings are consistent with the evidence of Gokcan (2000) that relatively parsimonious symmetric GARCH model is superior in forecasting the conditional variance of emerging stock market return series to the asymmetric GARCH model