Using a Genetic Program to Predict Exchange Rate Volatility

Created by W.Langdon from gp-bibliography.bib Revision:1.4394

  author =       "Christopher J. Neely and Paul A. Weller",
  title =        "Using a Genetic Program to Predict Exchange Rate
  booktitle =    "Genetic Algorithms and Genetic Programming in
                 Computational Finance",
  publisher =    "Kluwer Academic Publishers",
  year =         "2002",
  editor =       "Shu-Heng Chen",
  chapter =      "13",
  pages =        "263--279",
  address =      "Dordrecht",
  month =        jul,
  keywords =     "genetic algorithms, genetic programming, GARCH,
                 Foreign Exchange, Volatility, Forecasting,
  ISBN =         "0-7923-7601-3",
  URL =          "",
  DOI =          "doi:10.1007/978-1-4615-0835-9_13",
  abstract =     "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).",
  notes =        "Part of \cite{chen:2002:gagpcf}.

                 'A revised, improved version was published in the FRB
                 St. Louis Review' \cite{43-54NeelyWeller}.",

Genetic Programming entries for Christopher J Neely Paul A Weller