IDEAS home Printed from https://ideas.repec.org/a/cii/cepiie/2014-q1-137-6.html
   My bibliography  Save this article

Predicting exchange rates using a novel “cointegration based neuro-fuzzy system”

Author

Listed:
  • Behrooz Gharleghi
  • Abu Hassan Shaari
  • Najla Shafighi

Abstract

The present study focuses upon the applications of currently available intelligence techniques to forecast exchange rates in short and long horizons. The predictability of exchange rate returns is investigated through the use of a novel cointegration-based neuro-fuzzy system, which is a combination of a cointegration technique; a Fuzzy Inference System; and Artificial Neural Networks. The Relative Price Monetary Model for exchange rate determination is used to determine the inputs, consisting of macroeconomic variables and the type of interactions amongst the variables, in order to develop the system. Considering exchange rate returns of three ASEAN countries (Malaysia, the Philippines and Singapore), our results reveal that the cointegration-based neuro-fuzzy system model consistently outperforms the Vector Error Correction Model by successfully forecasting exchange rate monthly returns with a high level of accuracy.

Suggested Citation

  • Behrooz Gharleghi & Abu Hassan Shaari & Najla Shafighi, 2014. "Predicting exchange rates using a novel “cointegration based neuro-fuzzy system”," International Economics, CEPII research center, issue 137, pages 88-103.
    • Handle: RePEc:cii:cepiie:2014-q1-137-6
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S2110701713000528
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Oscar Claveria & Enric Monte & Petar Soric & Salvador Torra, 2022. "“An application of deep learning for exchange rate forecasting”," AQR Working Papers 202201, University of Barcelona, Regional Quantitative Analysis Group, revised Jan 2022.
    2. Chavan, Sumit Sunil & Shafighi, Najla, 2021. "Exchange Rate Determination in Asia," MPRA Paper 110622, University Library of Munich, Germany.
    3. Oscar Claveria & Enric Monte & Salvador Torra, 2016. "Modelling cross-dependencies between Spain’s regional tourism markets with an extension of the Gaussian process regression model," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 7(3), pages 341-357, August.
    4. Behrooz Gharleghi, 2023. "Debt and currency value during COVID‐19 in the Global South," Economic Affairs, Wiley Blackwell, vol. 43(2), pages 201-210, June.
    5. Leandro Maciel & Fernando Gomide & Rosangela Ballini, 2016. "Evolving Fuzzy-GARCH Approach for Financial Volatility Modeling and Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 48(3), pages 379-398, October.
    6. Dabin Zhang & Qian Li & Amin W. Mugera & Liwen Ling, 2020. "A hybrid model considering cointegration for interval‐valued pork price forecasting in China," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(8), pages 1324-1341, December.

    More about this item

    Keywords

    Exchange rate; Error correction model; Intelligence systems; Neural networks; Unit root;
    All these keywords.

    JEL classification:

    • E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation: Models and Applications
    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • F37 - International Economics - - International Finance - - - International Finance Forecasting and Simulation: Models and Applications

    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:cii:cepiie:2014-q1-137-6. 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: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/cepiifr.html .

    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.