VOLATILITY MODELING AND RISK ESTIMATION OF NIFTY 50 RETURNS ON THE INDIAN STOCK MARKET: AN APPLICATION OF GARCH MODELS

Abstract

This study focuses on the application of GARCH models to examine the return volatilities and risk characteristics of the Nifty 50 index, a key benchmark in the Indian financial market. Derivatives, which derive their value from underlying assets such as the Nifty 50, have become increasingly popular due to their relatively low capital requirements and strategic benefits for investors and financial managers. In order to explain the return volatilities of Nifty 50 index, we first discovered that a heavy-tailed distribution must be incorporated into the GARCH models. Next, we examine the VaR estimates using the parametric approaches, specifically the GARCH model with the normal inverse Gaussian distribution (GARCH-NIG) and GARCH model with the normal distribution (GARCH-Normal). Our findings show that while the GARCH-Normal model's VaR estimates are the most effective, the GARCH-NIG model's VaR estimates are the best. We conclude that the GARCH-NIG model has the potential to produce precise VaR estimates for the return series of Nifty 50 index. Additionally, we discovered that, unlike Nifty 50 index, the return volatilities of Nifty 50 index do not rise higher in reaction to positive shocks than to negative ones. Overall, our findings suggest that the GARCH-NIG model offers a robust framework for risk estimation and volatility modeling of the Nifty 50 index, with significant implications for portfolio management and financial risk assessment in the Indian equity market.