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Predicting exchange rate volatility: genetic programming versus GARCH and RiskMetrics

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  • Christopher J. Neely
  • Paul A. Weller

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.

Suggested Citation

  • Christopher J. Neely & Paul A. Weller, 2002. "Predicting exchange rate volatility: genetic programming versus GARCH and RiskMetrics," Review, Federal Reserve Bank of St. Louis, issue May, pages 43-54.
  • Handle: RePEc:fip:fedlrv:y:2002:i:may:p:43-54:n:v.84no.3
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    References listed on IDEAS

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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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    Cited by:

    1. Lux, Thomas & Kaizoji, Taisei, 2007. "Forecasting volatility and volume in the Tokyo Stock Market: Long memory, fractality and regime switching," Journal of Economic Dynamics and Control, Elsevier, vol. 31(6), pages 1808-1843, June.
    2. Aurea Grané & Helena Veiga, 2012. "Asymmetry, realised volatility and stock return risk estimates," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 11(2), pages 147-164, August.
    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. 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.
    6. 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.
    7. 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.

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