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Volatility Forecasting Using Explanatory Variables and Focused Selection Criteria

This paper assesses the performance of volatility forecasting using focused selection and combination strategies to include relevant explanatory variables in the forecasting model. The focused selection/combination strategies consist of picking up the model that minimizes the estimated risk (e.g. MSE) of a given smooth function of the parameters of interest to the forecaster. The proposed focused methods are compared with other strategies, including the well established AIC and BIC. The methodology is applied to a daily recursive 1--step ahead value--at--risk (VaR) forecasting exercise of 4 widely traded New York Stock Exchange stocks. Results show that VaR forecasts can significantly be improved upon using focused forecast strategies for the selection of relevant exogenous information. The set of explanatory variables that helps improving prediction is stock dependent. Traditional information criteria do not appear to be helpful in suggesting the inclusion of explanatory variables that actually improve prediction significantly. In line with recent theoretical findings, the predictive performance of the BIC appears to be modest.

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Paper provided by Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti" in its series Econometrics Working Papers Archive with number wp2007_04.

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Date of creation: May 2007
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Handle: RePEc:fir:econom:wp2007_04
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  15. Christian T. Brownlees & Giampiero Gallo, 2008. "Comparison of Volatility Measures: a Risk Management Perspective," Econometrics Working Papers Archive wp2008_03, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
  16. Lamoureux, Christopher G & Lastrapes, William D, 1990. " Heteroskedasticity in Stock Return Data: Volume versus GARCH Effects," Journal of Finance, American Finance Association, vol. 45(1), pages 221-29, March.
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  30. Christian T. Brownlees & Giampiero Gallo, 2007. "Flexible Time Series Forecasting Using Shrinkage Techniques and Focused Selection Criteria," Econometrics Working Papers Archive wp2007_02, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
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