Forecasting volatility

In this paper, we investigate the time series properties of S&P 100 volatility and the forecasting performance of different volatility models. We consider several nonparametric and parametric volatility measures, such as implied, realized and model-based volatility, and show that these volatility processes exhibit an extremely slow mean-reverting behavior and possible long memory. For this reason, we explicitly model the near-unit root behavior of volatility and construct median unbiased forecasts by approximating the finite-sample forecast distribution using bootstrap methods. Furthermore, we produce prediction intervals for the next-period implied volatility that provide important information about the uncertainty surrounding the point forecasts. Finally, we apply intercept corrections to forecasts from misspecified models which dramatically improve the accuracy of the volatility forecasts.