Non-linear, non-parametric, non-fundamental exchange rate forecasting

Jing Yang and Nikola Gradojevic
Additional contact information
Jing Yang: Financial Markets Department, Bank of Canada, Ottawa, Ontario, Canada, Postal: Financial Markets Department, Bank of Canada, Ottawa, Ontario, Canada

Journal of Forecasting, 2006, vol. 25, issue 4, pages 227-245

Abstract: This paper employs a non-parametric method to forecast high-frequency Canadian|US dollar exchange rate. The introduction of a microstructure variable, order flow, substantially improves the predictive power of both linear and non-linear models. The non-linear models outperform random walk and linear models based on a number of recursive out-of-sample forecasts. Two main criteria that are applied to evaluate model performance are root mean squared error (RMSE) and the ability to predict the direction of exchange rate moves. The artificial neural network (ANN) model is consistently better in RMSE to random walk and linear models for the various out-of-sample set sizes. Moreover, ANN performs better than other models in terms of percentage of correctly predicted exchange rate changes. The empirical results suggest that optimal ANN architecture is superior to random walk and any linear competing model for high-frequency exchange rate forecasting. Copyright © 2006 John Wiley & Sons, Ltd.

Downloads: (external link)
http://hdl.handle.net/10.1002/for.986 Link to full text; subscription required (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Access Statistics for this article

Journal of Forecasting is edited by Derek W. Bunn

More articles in Journal of Forecasting from John Wiley & Sons, Ltd.
Series data maintained by Christopher F. Baum ().