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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.

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Bibliographic Info

Article provided by Federal Reserve Bank of St. Louis in its journal Review.

Volume (Year): (2002)
Issue (Month): May ()
Pages: 43-54

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Handle: RePEc:fip:fedlrv:y:2002:i:may:p:43-54:n:v.84no.3

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Keywords: Foreign exchange rates ; Forecasting ; Programming (Mathematics);

References

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  1. Anderson, Torben G. & Bollerslev, Tim & Diebold, Francis X. & Labys, Paul, 2002. "Modeling and Forecasting Realized Volatility," Working Papers 02-12, Duke University, Department of Economics.
  2. Baillie, Richard T & Bollerslev, Tim, 1989. "The Message in Daily Exchange Rates: A Conditional-Variance Tale," Journal of Business & Economic Statistics, American Statistical Association, vol. 7(3), pages 297-305, July.
  3. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
  4. 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.
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Cited by:
  1. 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.
  2. Lux, Thomas & Kaizoji, Taisei, 2006. "Forecasting volatility and volume in the Tokyo stock market: Long memory, fractality and regime switching," Economics Working Papers 2006,13, Christian-Albrechts-University of Kiel, Department of Economics.
  3. 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.
  4. 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.

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