Forecasting daily and monthly exchange rates with machine learning techniques
We combine signal processing to machine learning methodologies by introducing a hybrid Ensemble Empirical Mode Decomposition (EEMD), Multivariate Adaptive Regression Splines (MARS) and Support Vector Regression (SVR) model in order to forecast the monthly and daily Euro (EUR)/United States Dollar (USD), USD/Japanese Yen (JPY), Australian Dollar (AUD)/Norwegian Krone (NOK), New Zealand Dollar (NZD)/Brazilian Real (BRL) and South African Rand (ZAR)/Philippine Peso (PHP) exchange rates. After the decomposition with EEMD of the original exchange rate series into a smoothed and a fluctuation component, MARS selects the most informative input datasets from the plethora of variables included in our initial data set. The selected variables are fed into two distinctive SVR models for forecasting each component separately one period ahead for daily and monthly data. The summation of the two forecasted components provides exchange rate forecasts. The above implementation exhibits superior forecasting ability in exchange rate forecasting compared to various models. Overall the proposed model a) is a combination of empirically proven effective techniques in forecasting time series, b) is data driven, c) relies on minimum initial assumptions and d) provides a structural aspect of the forecasting problem.
|Date of creation:||19 Mar 2013|
|Date of revision:||26 Sep 2013|
|Contact details of provider:|| Postal: Department of Economics, University Campus, Komotini, 69100, Greece|
Phone: (25310) 39.503
Fax: (25310) 39.502
Web page: http://www.econ.duth.gr/
More information through EDIRC
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.:
- Galbraith, John W. & KI[#x1e63]Inbay, Turgut, 2005. "Content horizons for conditional variance forecasts," International Journal of Forecasting, Elsevier, vol. 21(2), pages 249-260.
- Dornbusch, Rudiger, 1976. "Expectations and Exchange Rate Dynamics," Journal of Political Economy, University of Chicago Press, vol. 84(6), pages 1161-76, December.
- Hodrick, Robert J & Prescott, Edward C, 1997.
"Postwar U.S. Business Cycles: An Empirical Investigation,"
Journal of Money, Credit and Banking,
Blackwell Publishing, vol. 29(1), pages 1-16, February.
- Ken Matheny & Simon van Norden & Robert Vigfusson, 1989. "GAUSS code for the Hodrick-Prescott filter," QM&RBC Codes 2, Quantitative Macroeconomics & Real Business Cycles, revised Apr 1995.
- Christian Zimmermann, 2005. "HP-Filter code (Perl)," QM&RBC Codes 98, Quantitative Macroeconomics & Real Business Cycles.
- Edward C. Prescott, 1982. "FORTRAN code for the Hodrick-Prescott filter," QM&RBC Codes 3, Quantitative Macroeconomics & Real Business Cycles.
- Ivailo Izvorski, . "MATLAB code for the Hodrick-Prescott filter," QM&RBC Codes 1, Quantitative Macroeconomics & Real Business Cycles.
- Kurt Annen, 2006. "HP-Filter DLL executable," QM&RBC Codes 167, Quantitative Macroeconomics & Real Business Cycles.
- Kurt Annen, 2006. "HP-Filter Excel Add-In," QM&RBC Codes 165, Quantitative Macroeconomics & Real Business Cycles.
- Morten Ravn, . "GAUSS program for Hodrick-Prescott filter," QM&RBC Codes 101, Quantitative Macroeconomics & Real Business Cycles.
- Kurt Annen, 2004. "HP-filter for Java," QM&RBC Codes 168, Quantitative Macroeconomics & Real Business Cycles.
- Robert J. Hodrick & Edward Prescott, 1981. "Post-War U.S. Business Cycles: An Empirical Investigation," Discussion Papers 451, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
- Kurt Annen, 2004. "Matlab functions for HP-filter," QM&RBC Codes 166, Quantitative Macroeconomics & Real Business Cycles.
- Morten Ravn, . "Alternate GAUSS program for the Hodrick-Prescott Filter," QM&RBC Codes 102, Quantitative Macroeconomics & Real Business Cycles.
- Christian Zimmermann, 2005. "HP-Filter (web interface)," QM&RBC Codes 97, Quantitative Macroeconomics & Real Business Cycles.
- Theodore Alexandrov & Silvia Bianconcini & Estela Bee Dagum & Peter Maass & Tucker S. McElroy, 2012. "A Review of Some Modern Approaches to the Problem of Trend Extraction," Econometric Reviews, Taylor & Francis Journals, vol. 31(6), pages 593-624, November.
- Frankel, Jeffrey A, 1979. "On the Mark: A Theory of Floating Exchange Rates Based on Real Interest Differentials," American Economic Review, American Economic Association, vol. 69(4), pages 610-22, September.
- Cheung, Yin-Wong, 1993. "Long Memory in Foreign-Exchange Rates," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(1), pages 93-101, January.
- Benjamin J. C. Kim & David Karemera, 2006. "Assessing the forecasting accuracy of alternative nominal exchange rate models: the case of long memory," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(5), pages 369-380.
When requesting a correction, please mention this item's handle: RePEc:ris:duthrp:2013_003. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Periklis Gogas)
If references are entirely missing, you can add them using this form.