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.

Prediction and Clustering Techniques used in the Development of Stock Forecasting Model

“…To build an effective prediction model one should consider the volatility factors. In this regard [41] have proposed a forecasting model for volatility in particularly conditional variance and the model was tested on Indian stock market index SENSEX data over a decade from 1996 to 2010. In the proposed model, Generalized Autoregressive Conditional Heteroskedasticity (GARCH), Threshold GARCH, and Exponential GARCH were considered for the study and the results displayed that the GARCH models were performing superior in forecasting the conditional variance in Indian stock exchange [42].…”

Sectoral Analysis of Bombay Stock Exchange – Application of Garch Models

“…Srinivasan & Ibrahim (2010) forecasted Stock Market Volatility of BSE-30 Index with the help of GARCH models based on the daily data and results led to the conclusion that symmetric GARCH (1,1) model performed better in forecasting the conditional variance of the Sensex daily returns compared to EGARCH (1,1) and TGARCH (1,1) models. Khemiri (2011) analysed the dynamics of four international stock indices by applying Smooth Transition GARCH (STGARCH) model.…”

Modelling Volatility of BSE Realty Index using Conditional Heteroscedasticity Models

“…Goudarzi and Ramanarayanan (2010) studied the nature of volatility of the BSE 500 and found that the volatility is persistent and exhibits clustering. Srinivasan and Ibrahim (2010) examined the volatility in BSE Sensex and reported the presence of leverage effect in the volatility. However, the risk-return tradeoff is not significantly evident in Indian stock market (Banumathy and Ramachandran, 2015, Kumar and Singh, 2008, Karmakar, 2007.…”