Modelling Stock Market Volatility – A Study of S&P CNX NIFTY

Abstract

Over the last few years, modelling and forecasting volatility of a financial time series has become a fertile area for research. This is simply because of the fact that volatility is considered as an important concept for many economic and financial applications. The examples may refer to the portfolio optimization, risk management and asset pricing etc. Many financial time series display volatility clustering, implied in Autoregressive Conditional Heteroskedasticty (ARCH). This paper applies the Generalized Autoregressive Conditional Heteroskedastic models to estimate volatility (conditional variance) in the daily returns of National Stock Exchange (NSE) over the period from July 1990 to September 2013. The estimation of volatility is made at the macro level on the market index, namely S&P CNX Nifty. The GARCH (1, 1) and GARCH-M (1, 1) models have been fitted to the market index. The empirical result shows that the conditional variance process is a strong evidence of time-varying volatility, volatility clustering, and a highly persistence (explosive process). These models also provide the evidence on the existence of risk premium for the S&P CNX Nifty index return series. This study supports the hypothesis of positive correlation between volatility and expected stock return.