Modeling the Effect of NASDAQ and Index Futures Introduction on the Volatility of NSE Nifty Using GARCH Methodology

Student Name: K. Kiran Kumar

The first problem of interest was to study causal issues of mean returns and return volatilities between NSE, India and NASDAQ, USA and then empirically model this dynamics using univariate ARMA-GARCH, two-stage ARMA-GARCH and multivariate ARMA-GARCH techniques. The study found both linear and non-linear Granger causality running unidirectionally from USA to India with respect to both mean returns and their volatilities. Though two-stage ARMA-GARCH models, which are special cases of multivariate ARMA-GARCH models, are advocated in the literature for such equity market inter-linkages study with markets having non-overlapping trading hours, as in the case of NSE and NASDAQ, this study found that a much simpler univariate ARMA-GARCH model is equally capable of capturing the same dynamics with fewer number of clearly interpretable parameters. The in-sample validation and out-of-sample forecast performance did not significantly improve after applying the multivariate ARMA-GARCH technique either, which yielded qualitatively similar results. The major findings of this study was that the mean overnight returns of NSE Nifty are getting affected by both the daytime returns of NSE Nifty and NASDAQ composite of the previous day, while its volatility seems to be affected only by the NASDAQ. For the next problem, the daily returns of NSE Nifty were first subjected to an informal graphical and formal Bayesian and likelihood based change-point analysis to confirm that indeed there is a shift in its volatility post Futures introduction. Then this shift was modeled using a switching ARMA-GARCH model. The study found that recent news is being assimilated in the volatility at a much faster rate in the post Futures introduction period, with a substantial reduction in its persistence.