Abstract
This article illustrates the strengths and weaknesses of genetic programming in the context of forecasting out-of-sample volatility in the DEM/USD and JPY/USD markets. GARCH(1,1) models serve used as a benchmark. While the GARCH model outperforms the genetic program at short horizons using the mean-squared-error (MSE) criterion, the genetic program often outperforms the GARCH at longer horizons and consistently returns lower mean absolute forecast errors (MAE).
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© 2002 Springer Science+Business Media New York
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Neely, C.J., Weller, P.A. (2002). Using a Genetic Program to Predict Exchange Rate Volatility. In: Chen, SH. (eds) Genetic Algorithms and Genetic Programming in Computational Finance. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-0835-9_13
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DOI: https://doi.org/10.1007/978-1-4615-0835-9_13
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4613-5262-4
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