- author = "Christopher J. Neely and Paul A. Weller",
- title = "Using a Genetic Program to Predict Exchange Rate Volatility",
- 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, Heteroskedasticity",
- ISBN = "0-7923-7601-3",
-
URL = "
http://www.springer.com/economics/economic+theory/book/978-0-7923-7601-9",
-
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}.",
