Forecasting volatility

Athanasia Gavala, Nikolay Gospodinov () and Deming Jiang
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Athanasia Gavala: Department of Economics, Concordia University, Montreal, Quebec, Canada, Postal: Department of Economics, Concordia University, Montreal, Quebec, Canada
Deming Jiang: Department of Economics, Concordia University, Montreal, Quebec, Canada, Postal: Department of Economics, Concordia University, Montreal, Quebec, Canada

Journal of Forecasting, 2006, vol. 25, issue 6, pages 381-400

Abstract: 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.  Copyright © 2006 John Wiley & Sons, Ltd.

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