Predicting exchange rate volatility: genetic programming versus GARCH and RiskMetrics
AbstractThis 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, the Deutsche mark and the Japanese yen. Although the GARCH and RiskMetrics‘ models appear to have an 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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoArticle provided by Federal Reserve Bank of St. Louis in its journal Review.
Volume (Year): (2002)
Issue (Month): May ()
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2003.
"Modeling and Forecasting Realized Volatility,"
Econometric Society, vol. 71(2), pages 579-625, March.
- Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2001. "Modeling and Forecasting Realized Volatility," Center for Financial Institutions Working Papers 01-01, Wharton School Center for Financial Institutions, University of Pennsylvania.
- Anderson, Torben G. & Bollerslev, Tim & Diebold, Francis X. & Labys, Paul, 2002. "Modeling and Forecasting Realized Volatility," Working Papers 02-12, Duke University, Department of Economics.
- Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2001. "Modeling and Forecasting Realized Volatility," NBER Working Papers 8160, National Bureau of Economic Research, Inc.
- Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
- Baillie, Richard T & Bollerslev, Tim, 1989.
"The Message in Daily Exchange Rates: A Conditional-Variance Tale,"
Journal of Business & Economic Statistics,
American Statistical Association, vol. 7(3), pages 297-305, July.
- Baillie, Richard T & Bollerslev, Tim, 2002. "The Message in Daily Exchange Rates: A Conditional-Variance Tale," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 60-68, January.
- Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
- Lux, Thomas & Kaizoji, Taisei, 2006.
"Forecasting volatility and volume in the Tokyo stock market: Long memory, fractality and regime switching,"
Economics Working Papers
2006,13, Christian-Albrechts-University of Kiel, Department of Economics.
- Lux, Thomas & Kaizoji, Taisei, 2007. "Forecasting volatility and volume in the Tokyo Stock Market: Long memory, fractality and regime switching," Journal of Economic Dynamics and Control, Elsevier, vol. 31(6), pages 1808-1843, June.
- Taisei Kaizoji & Thomas Lux, 2006. "Forecasting Volatility and Volume in the Tokyo Stock Market: Long Memory, Fractality and Regime Switching," Working Papers wp06-20, Warwick Business School, Financial Econometrics Research Centre.
- Dr. Ioannis N. Kallianiotis & Dr. Dean Frear, 2006. "Assets Return and Risk and Exchange Rate Trends: An Ex Post Analysis," European Research Studies Journal, European Research Studies Journal, vol. 0(3-4), pages 15-34.
- Nunez-Letamendia, Laura, 2007. "Fitting the control parameters of a genetic algorithm: An application to technical trading systems design," European Journal of Operational Research, Elsevier, vol. 179(3), pages 847-868, June.
- Syouching Lai & Hungchih Li, 2006. "The predictive power of quarterly earnings per share based on time series and artificial intelligence model," Applied Financial Economics, Taylor and Francis Journals, vol. 16(18), pages 1375-1388.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Xiao Xiaohong) The email address of this maintainer does not seem to be valid anymore. Please ask Xiao Xiaohong to update the entry or send us the correct address.
If references are entirely missing, you can add them using this form.