Forecasting performance of extreme-value volatility estimators

This study evaluates the forecasting performance of extreme-value volatility estimators for the equity-based Nifty Index using two-scale realized volatility. This benchmark mitigates the effect of microstructure noise in the realized volatility. Extreme-value estimates with relatively simple forecasting methods provide substantially better short-term and long-term forecasts, compared to historical volatility. The higher efficiency of extreme-value estimators is primarily responsible for this improvement. The extent of possible improvement in forecasts is likely to be economically significant for applications like options pricing. By including extreme-value estimators, the forecasting performance of generalized autoregressive conditional heteroscedasticity (GARCH) can also be improved. (c) 2007 Wiley Periodicals, Inc.