IDEAS home Printed from https://ideas.repec.org/p/ris/duthrp/2013_003.html
   My bibliography  Save this paper

Forecasting daily and monthly exchange rates with machine learning techniques

Author

Listed:
  • 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.

Suggested Citation

  • Papadimitriou, Theophilos & Gogas, Periklis & Plakandaras, Vasilios, 2013. "Forecasting daily and monthly exchange rates with machine learning techniques," DUTH Research Papers in Economics 3-2013, Democritus University of Thrace, Department of Economics, revised 07 Apr 2015.
  • Handle: RePEc:ris:duthrp:2013_003
    as

    Download full text from publisher

    File URL: http://utopia.duth.gr/~vplakand/WP%20Forecasting%20daily%20and%20monthly%20exchange%20rates%20final.pdf
    File Function: Full text
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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 610-622, September.
    5. 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.
    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. Dornbusch, Rudiger, 1976. "Expectations and Exchange Rate Dynamics," Journal of Political Economy, University of Chicago Press, vol. 84(6), pages 1161-1176, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Vasilios Plakandaras & Theophilos Papadimitriou & Periklis Gogas & Konstantinos Diamantaras, 2014. "Market Sentiment and Exchange Rate Directional Forecasting," Working Paper series 37_14, Rimini Centre for Economic Analysis.
    2. Rangan Gupta & Tahir Suleman & Mark E. Wohar, 2017. "Exchange Rate Returns and Volatility: The Role of Time-Varying Rare Disaster Risks," Working Papers 201767, University of Pretoria, Department of Economics.
    3. Vasilios Plakandaras & Periklis Gogas & Theophilos Papadimitriou & Rangan Gupta, 2016. "The Term Premium as a Leading Macroeconomic Indicator," Working Papers 201613, University of Pretoria, Department of Economics.
    4. Plakandaras, Vasilios & Gupta, Rangan & Wohar, Mark E., 2017. "The depreciation of the pound post-Brexit: Could it have been predicted?," Finance Research Letters, Elsevier, vol. 21(C), pages 206-213.

    More about this item

    Keywords

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

    JEL classification:

    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. 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). General contact details of provider: http://edirc.repec.org/data/didutgr.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.