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Predicting exchange rate volatility: genetic programming versus GARCH and RiskMetrics

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Abstract

This article investigates the use of genetic programming to forecast out-of-sample daily volatility in the foreign exchange market. Forecasting performance is evaluated relative to GARCH(1,1) and RiskMetrics? models for two currencies, the Deutsche mark and the Japanese yen. Although the GARCH and RiskMetrics? models appear to have an inconsistent marginal edge over the genetic program using the mean-squared-error (MSE) and R2 criteria, the genetic program consistently produces lower mean absolute forecast errors (MAE) at all horizons and for both currencies.

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  • Christopher J. Neely & Paul A. Weller, 2002. "Predicting exchange rate volatility: genetic programming versus GARCH and RiskMetrics," Review, Federal Reserve Bank of St. Louis, vol. 84(May), pages 43-54.
    • Handle: RePEc:fip:fedlrv:y:2002:i:may:p:43-54:n:v.84no.3
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    1. Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
    2. Baillie, Richard T & Bollerslev, Tim, 2002. "The Message in Daily Exchange Rates: A Conditional-Variance Tale," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 60-68, January.
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    4. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2003. "Modeling and Forecasting Realized Volatility," Econometrica, Econometric Society, vol. 71(2), pages 579-625, March.
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