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

  • Jing Yang

    (Financial Markets Department, Bank of Canada, Ottawa, Ontario, Canada)

  • Nikola Gradojevic

    (Faculty of Business Administration, Lakehead University, Thunder Bay, Ontario, Canada)

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.

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File URL: http://hdl.handle.net/10.1002/for.986
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Article provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.

Volume (Year): 25 (2006)
Issue (Month): 4 ()
Pages: 227-245

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Handle: RePEc:jof:jforec:v:25:y:2006:i:4:p:227-245
DOI: 10.1002/for.986
Contact details of provider: Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966

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