Predicting Exchange Rate Volatility: Genetic Programming vs. GARCH and Risk Metrics

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@TechReport{neely:2001-009B,
  author =       "Christopher J. Neely and Paul A. Weller",
  title =        "Predicting Exchange Rate Volatility: Genetic
                 Programming vs. GARCH and Risk Metrics",
  institution =  "Economic, Research, Federal Reserve Bank of St.
                 Louis",
  year =         "2001",
  type =         "Working Paper",
  number =       "2001-009B",
  address =      "411 Locust Street, St. Louis, MO 63102-0442, USA",
  month =        sep,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://research.stlouisfed.org/wp/2001/2001-009.pdf",
  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.",
  size =         "30 pages",
}

Genetic Programming entries for Christopher J Neely Paul A Weller

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