Forecasting Stock Market Volatility of Bse-30 Index Using Garch Models

Abstract

Volatility forecasting is an important area of research in financial markets and immense effort has been made 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 to modelling and forecasting the volatility (conditional variance) of the SENSEX Index returns of Indian stock market, using daily data, covering a period from 1st January 1996 to 29th January 2010. The forecasting models that are considered in this study range from the relatively 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 do perform better in forecasting conditional variance of the SENSEX Index return rather than the asymmetric GARCH models, despite the presence of leverage effect. Findings of the study are consistent with the evidence of Deb, Vuyyuri and Roy (2003) that parsimonious symmetric GARCH model is found superior in forecasting the conditional variance of SENSEX Index market returns rather than the asymmetric GARCH models.