Forecasting Stock Market Volatility using GARCH Models: Evidence from the Indian Stock Market

Abstract

The present article attempts to modelling and forecasting the volatility of the BSE-SENSEX Index returns of Indian stock market, using daily data covering a period from 1, July 1997 to 31, December 2015. The forecasting models that are considered in this study range from the symmetric GARCH (1,1) model to asymmetric GARCH models (including Exponential GARCH (1,1) and Threshold GARCH (1,1) models). On the basis of out of the sample forecasts and a majority of evaluation measures our result shows that the asymmetric GARCH models do perform better in forecasting conditional variance of the BSE-SENSEX returns rather than the symmetric GARCH model, confirming the presence of leverage effect. Our findings are consistent with the evidence of Banerjee and Sarkar (2006) and Srinivasan (2015) that reveals relatively asymmetric GARCH models are found superior in forecasting the conditional variance of Indian stock market returns rather than the parsimonious symmetric GARCH models.