IDEAS home Printed from https://ideas.repec.org/p/bog/wpaper/49.html
   My bibliography  Save this paper

Computational Intelligence in Exchange-Rate Forecasting

Author

Listed:
  • Andreas S. Andreou

    (University of Cyprus)

  • George A. Zombanakis

    () (Bank of Greece)

Abstract

This paper applies computational intelligence methods to exchange rate forecasting. In particular, it employs neural network methodology in order to predict developments of the Euro exchange rate versus the U.S. Dollar and the Japanese Yen. Following a study of our series using traditional as well as specialized, non-parametric methods together with Monte Carlo simulations we employ selected Neural Networks (NNs) trained to forecast rate fluctuations. Despite the fact that the data series have been shown by the Rescaled Range Statistic (R/S) analysis to exhibit random behaviour, their internal dynamics have been successfully captured by certain NN topologies, thus yielding accurate predictions of the two exchange-rate series.

Suggested Citation

  • Andreas S. Andreou & George A. Zombanakis, 2006. "Computational Intelligence in Exchange-Rate Forecasting," Working Papers 49, Bank of Greece.
  • Handle: RePEc:bog:wpaper:49
    as

    Download full text from publisher

    File URL: http://www.bankofgreece.gr/BogEkdoseis/Paper200649.pdf
    File Function: Full Text
    Download Restriction: no

    References listed on IDEAS

    as
    1. Hans Genberg, 2006. "Exchange-rate arrangements and financial integration in East Asia: on a collision course?," International Economics and Economic Policy, Springer, vol. 3(3), pages 359-377, December.
    2. repec:wsi:wschap:9789813148543_0011 is not listed on IDEAS
    3. Kilian, Lutz & Taylor, Mark P., 2003. "Why is it so difficult to beat the random walk forecast of exchange rates?," Journal of International Economics, Elsevier, vol. 60(1), pages 85-107, May.
    4. Pesaran, M Hashem & Potter, Simon M, 1992. "Nonlinear Dynamics and Econometrics: An Introduction," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 7(S), pages 1-7, Suppl. De.
    5. Panayotis Kapopoulos & Sophia Lazaretou, 2009. "Does corporate ownership structure matter for economic growth? A cross-country analysis," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 30(3), pages 155-172.
    6. Charles Engel & Kenneth D. West, 2004. "Accounting for Exchange Rate Variability in Present-Value Models When the Discount Factor is Near One," NBER Working Papers 10267, National Bureau of Economic Research, Inc.
    7. Marsh, Ian W. & Power, David M., 1996. "A note on the performance of foreign exchange forecasters in a portfolio framework," Journal of Banking & Finance, Elsevier, vol. 20(3), pages 605-613, April.
    8. West, Kenneth D. & Cho, Dongchul, 1995. "The predictive ability of several models of exchange rate volatility," Journal of Econometrics, Elsevier, vol. 69(2), pages 367-391, October.
    9. Meese, Richard A. & Rogoff, Kenneth, 1983. "Empirical exchange rate models of the seventies : Do they fit out of sample?," Journal of International Economics, Elsevier, vol. 14(1-2), pages 3-24, February.
    10. Martin D. D. Evans & Richard K. Lyons, 2017. "Meese-Rogoff Redux: Micro-Based Exchange-Rate Forecasting," World Scientific Book Chapters,in: Studies in Foreign Exchange Economics, chapter 11, pages 457-475 World Scientific Publishing Co. Pte. Ltd..
    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. Alexandros E. Milionis, 2006. "An Alternative Definition of Market Efficiency and some Comments on its Empirical Testing," Working Papers 50, Bank of Greece.
    2. Tamal Datta Chaudhuri & Indranil Ghosh, 2016. "Artificial Neural Network and Time Series Modeling Based Approach to Forecasting the Exchange Rate in a Multivariate Framework," Papers 1607.02093, arXiv.org.
    3. Kristoufek, Ladislav, 2009. "Distinguishing between short and long range dependence: Finite sample properties of rescaled range and modified rescaled range," MPRA Paper 16424, University Library of Munich, Germany.

    More about this item

    Keywords

    Exchange - rate forecasting; Neural networks;

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

    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:bog:wpaper:49. 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: (Christina Tsochatzi). General contact details of provider: http://edirc.repec.org/data/boggvgr.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.