Econometric Modeling of Macroeconomic Determinants of Stock Market Volatility in India with Special Reference to NSEIL

Modeling and forecasting volatility of a financial time series has become a very prominent area for research last few years. These models provide precise estimate of conditional variance process and make a good forecast of future volatility that may help the stakeholders in obtaining efficient portfolio and accurate derivative prices of financial instruments. This paper aims at developing an econometric model for predicting stock market variability affected due to variations in the macroeconomic indicators. This paper considered twelve years’ monthly data of Returns of Monthly Averages of S&P CNX Nifty (NRTS) as dependent variable and fifteen independent macroeconomic variables selected from different segments of economy. In the process, variables as described in the econometric function for stock market returns at NSE are first tested for unit root and stationary and then causal links among dependent and independent variables are explored by using Granger causality in bi-variate and multivariate VAR framework. The Multivariate GARCH models developed for predicting NRTS affected due to variations in various sets of macroeconomic variables indicate that though these models are capable of measuring the impact of changes in one/ set of series on the other series of same amplitude, but, are suitable in short period only