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Forecasting daily and monthly exchange rates with machine learning techniques

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  • Papadimitriou, Theophilos

    ()
    (Democritus University of Thrace, Department of International Economic Relations and Development)

  • Gogas, Periklis

    ()
    (Democritus University of Thrace, Department of International Economic Relations and Development)

  • Plakandaras, Vasilios

    ()
    (Democritus University of Thrace, Department of International Economic Relations and Development)

Abstract

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.

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File URL: http://utopia.duth.gr/~vplakand/Forecasting%20daily%20and%20monthly%20exchange%20rates%20with%20machine%20learning%20techniques.pdf
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Bibliographic Info

Paper provided by Democritus University of Thrace, Department of Economics in its series DUTH Research Papers in Economics with number 3-2013.

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Length: 32 pages
Date of creation: 19 Mar 2013
Date of revision: 26 Sep 2013
Handle: RePEc:ris:duthrp:2013_003

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Postal: Department of Economics, University Campus, Komotini, 69100, Greece
Phone: (25310) 39.503
Fax: (25310) 39.502
Web page: http://www.econ.duth.gr/
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Related research

Keywords: Exchange rate forecasting; Support Vector Regression; local learning; feature selection; Ensemble Empirical Mode Decomposition; time series; trend;

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  1. Dornbusch, Rudiger, 1976. "Expectations and Exchange Rate Dynamics," Journal of Political Economy, University of Chicago Press, vol. 84(6), pages 1161-76, December.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
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