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Medium‐term horizon volatility forecasting: A comparative study

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  • Richard Hawkes
  • Paresh Date

Abstract

In this paper, volatility is estimated and then forecast using unobserved components‐realized volatility (UC‐RV) models as well as constant volatility and GARCH models. With the objective of forecasting medium‐term horizon volatility, various prediction methods are employed: multi‐period prediction, variable sampling intervals and scaling. The optimality of these methods is compared in terms of their forecasting performance. To this end, several UC‐RV models are presented and then calibrated using the Kalman filter. Validation is based on the standard errors on the parameter estimates and a comparison with other models employed in the literature such as constant volatility and GARCH models. Although we have volatility forecasting for the computation of Value‐at‐Risk in mind the methodology presented has wider applications. This investigation into practical volatility forecasting complements the substantial body of work on realized volatility‐based modelling in business. Copyright © 2007 John Wiley & Sons, Ltd.

Suggested Citation

  • Richard Hawkes & Paresh Date, 2007. "Medium‐term horizon volatility forecasting: A comparative study," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 23(6), pages 465-481, November.
    • Handle: RePEc:wly:apsmbi:v:23:y:2007:i:6:p:465-481
    DOI: 10.1002/asmb.684
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