IDEAS home Printed from https://ideas.repec.org/a/taf/applec/v40y2008i15p1969-1984.html
   My bibliography  Save this article

Exchange rates forecasting: local or global methods?

Author

Listed:
  • Marcos Alvarez-Diaz

Abstract

Exchange rates forecasters usually assume that local methods (nearest neighbour) dominate the global ones (neural networks or genetic programming, for example). In this article, first, we use different generalizations of the standard nearest neighbours to predict the dynamic evolution of the Yen/US$ and Pound Sterling/US$ exchange rates one-period ahead. Second, we compare our results with those employing global methods such as neural networks, genetic programming, data fusion and evolutionary neural networks. Finally, we find out the existence of predictable structures τ periods ahead. Our results reveal a slightly but significant forecasting ability for one-period ahead which is lost when more periods ahead are considered, and no important predictive differences between local and global methods have been found.

Suggested Citation

  • Marcos Alvarez-Diaz, 2008. "Exchange rates forecasting: local or global methods?," Applied Economics, Taylor & Francis Journals, vol. 40(15), pages 1969-1984.
    • Handle: RePEc:taf:applec:v:40:y:2008:i:15:p:1969-1984
    DOI: 10.1080/00036840600905308
    as

    Download full text from publisher

    File URL: http://www.tandfonline.com/doi/abs/10.1080/00036840600905308
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00036840600905308?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

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


    Cited by:

    1. M. Ali Choudhary & Adnan Haider, 2012. "Neural network models for inflation forecasting: an appraisal," Applied Economics, Taylor & Francis Journals, vol. 44(20), pages 2631-2635, July.
    2. Firat Melih Yilmaz & Ozer Arabaci, 2021. "Should Deep Learning Models be in High Demand, or Should They Simply be a Very Hot Topic? A Comprehensive Study for Exchange Rate Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 217-245, January.
    3. Kück, Mirko & Freitag, Michael, 2021. "Forecasting of customer demands for production planning by local k-nearest neighbor models," International Journal of Production Economics, Elsevier, vol. 231(C).
    4. Marcos Álvarez-Díaz & Shawkat Hammoudeh & Rangan Gupta, 2013. "Detecting Predictable Non-linear Dynamics in Dow Jones Industrial Average and Dow Jones Islamic Market Indices using Nonparametric Regressions," Working Papers 201385, University of Pretoria, Department of Economics.

    More about this item

    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:taf:applec:v:40:y:2008:i:15:p:1969-1984. See general information about how to correct material in RePEc.

    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.

    We have no bibliographic references for this item. You can help adding them by using 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/RAEC20 .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.