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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. 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.
    2. 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.
    3. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    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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    2. Ioannis N. Kallianiotis & Karen Bianchi & Augustine C. Arize & John Malindretos & Ikechukwu Ndu, 2020. "Financial Assets, Expected Return and Risk, Speculation, Uncertainty, and Exchange Rate Determination," European Research Studies Journal, European Research Studies Journal, vol. 0(3), pages 3-30.
    3. Christian Bauer & Bernhard Herz, 2004. "Technical trading and the volatility of exchange rates," Quantitative Finance, Taylor & Francis Journals, vol. 4(4), pages 399-415.
    4. Nunez-Letamendia, Laura, 2007. "Fitting the control parameters of a genetic algorithm: An application to technical trading systems design," European Journal of Operational Research, Elsevier, vol. 179(3), pages 847-868, June.
    5. Syouching Lai & Hungchih Li, 2006. "The predictive power of quarterly earnings per share based on time series and artificial intelligence model," Applied Financial Economics, Taylor & Francis Journals, vol. 16(18), pages 1375-1388.
    6. Raphael I. Udegbunam & Hassan E. Oaikhenan, 2012. "Interest Rate Risk of Stock Prices in Nigeria," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 11(1), pages 93-113, April.
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    8. Dr. Ioannis N. Kallianiotis & Dr. Dean Frear, 2006. "Assets Return and Risk and Exchange Rate Trends: An Ex Post Analysis," European Research Studies Journal, European Research Studies Journal, vol. 0(3-4), pages 15-34.
    9. Wagner Neal F & Thompson Mark A, 2009. "Forecasting the Periodic Net Discount Rate with Genetic Programming," Journal of Business Valuation and Economic Loss Analysis, De Gruyter, vol. 4(1), pages 1-15, October.

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