Investigating Impact of Volatility Persistence and Information Inflow on Volatility of Stock Indices Using Bivarite GJR-GARCH

Joint dynamics of market index returns, volume traded and volatility of stock market returns can unveil different dimensions of market microstructure. In this study, the joint dynamics is investigated with the help of bivarite Glosten–Jagannathan–Runkle generalized autoregressive conditional heteroskedasticity (GJR-GARCH) methodology given by Bollerslev (1990), as this method helps in jointly estimating volatility equation of return and volume in a one-step estimation procedure and it also eliminates the regressor problem (Pagan, 1984). The study finds negative conditional correlation between volume traded and return of large-cap index. The relation between volume traded and volatility is found to be positive in case of large-cap index but it is negative in the case of mid-cap and small-cap indices. Volatility is affected by pronounced persistence in volatility, mean-reversion of returns and asymmetry in market. The rate of information arrival measured by intra-day volatility (IDV) is found to be a significant source of the conditional heteroskedasticity in Indian markets since the presence of volume (proxy for information flow) in volatility equation, as an independent variable, marginally reduces the volatility persistence, whereas presence of IDV, as a proxy for information flow, completely makes GARCH effect insignificant.