Volatility Persistence In The Presence Of Structural Breaks In The Indian Banking Sector

In this paper, we investigate the impact of regime shifts on the persistence of volatility from the vantage point of modeling volatility in general and, in particular, in assessing the forecasting ability of GARCH models in the context of the Indian banking sector. We apply the Iterated Cumulative Sums of Squares (ICSS) algorithm to identify the points of sudden changes in the volatility of the data series. We find that when endogenously determined regime shifts in the variance are incorporated in the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model, the estimated persistence in the volatility of returns comes down drastically. This suggests that ignoring regime shifts in the model may result in an overestimation of the persistence of volatility. In addition, we find that sudden changes in the variance are largely associated with domestic and global macroeconomic and political events. The out-of-sample forecast evaluation analysis confirms that volatility models that incorporate regime shifts provide more accurate one-step-ahead volatility forecasts than their counterparts without regime shifts. These findings have important policy implications for financial market participants, investors and policy makers.