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Predicting exchange rate volatility: genetic programming vs. 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, DEM and JPY. Although the GARCH/RiskMetrics models appear to have a 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, 2001. "Predicting exchange rate volatility: genetic programming vs. GARCH and RiskMetrics," Working Papers 2001-009, Federal Reserve Bank of St. Louis.
    • Handle: RePEc:fip:fedlwp:2001-009
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    1. Neely, Christopher & Weller, Paul & Dittmar, Rob, 1997. "Is Technical Analysis in the Foreign Exchange Market Profitable? A Genetic Programming Approach," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 32(4), pages 405-426, December.
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    Cited by:

    1. Neely, Christopher J., 2009. "Forecasting foreign exchange volatility: Why is implied volatility biased and inefficient? And does it matter?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 19(1), pages 188-205, February.
    2. Christopher J. Neely, 2004. "Implied volatility from options on gold futures: do statistical forecasts add value or simply paint the lilly?," Working Papers 2003-018, Federal Reserve Bank of St. Louis.
    3. Osei-Assibey, Kwame, 2016. "Revisiting the Diverse Empirical Findings on the Impact of Exchange Rate Volatility on Trade: Some Comparable Evidences from Ghana and Two other Developing Economies," MPRA Paper 94368, University Library of Munich, Germany.

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    Keywords

    Foreign exchange rates; Forecasting; Programming (Mathematics);
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