IDEAS home Printed from https://ideas.repec.org/a/kap/compec/v9y1996i1p51-65.html
   My bibliography  Save this article

A Neural Network Approach to Long-Run Exchange Rate Prediction

Author

Listed:
  • Verkooijen, William

Abstract

In the economics literature on exchange rate determination, no theory has yet been found that performs well in out-of-sample prediction experiments. Until today, the simple random walk model has never been significantly outperformed. We have identified a set of fundamental long-run exchange rate models from literature that are well-known among economists. This paper investigates whether a neural network representation of these structural exchange rate models improves the out-of-sample prediction performance of the linear versions. Empirical results are reported in the case of the U.S. dollar-Deutsche Mark exchange rate. Citation Copyright 1996 by Kluwer Academic Publishers.

Suggested Citation

  • Verkooijen, William, 1996. "A Neural Network Approach to Long-Run Exchange Rate Prediction," Computational Economics, Springer;Society for Computational Economics, vol. 9(1), pages 51-65, February.
    • Handle: RePEc:kap:compec:v:9:y:1996:i:1:p:51-65
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Citations

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


    Cited by:

    1. Knobel, Alexander (Кнобель, Александр) & Kazaryan, Margarita (Казарян, Маргарита) & Kuznetsov, D.E. (Кузнецов, Д.Е.) & Sedalishchev, V.V. (Седалищев, В.В.) & Firanchuk, Alexander (Фиранчук, Александр), 2016. "Economic Benefits, Costs and Risks for Russia on Trade and Economic Alliances with Countries of CIS, Europe and the USA Contact [Экономические Выгоды, Издержки И Риски Для России От Торгово-Экономи," Working Papers 1854, Russian Presidential Academy of National Economy and Public Administration.
    2. Jing Yang & Nikola Gradojevic, 2006. "Non-linear, non-parametric, non-fundamental exchange rate forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(4), pages 227-245.
    3. Nikola Gradojevic & Jing Yang, 2000. "The Application of Artificial Neural Networks to Exchange Rate Forecasting: The Role of Market Microstructure Variables," Staff Working Papers 00-23, Bank of Canada.
    4. Panda, Chakradhara & Narasimhan, V., 2007. "Forecasting exchange rate better with artificial neural network," Journal of Policy Modeling, Elsevier, vol. 29(2), pages 227-236.

    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:kap:compec:v:9:y:1996:i:1:p:51-65. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.