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

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

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Article ID: iaor20081077
Country: United Kingdom
Volume: 25
Issue: 4
Start Page Number: 227
End Page Number: 245
Publication Date: Jul 2006
Journal: International Journal of Forecasting
Authors: ,
Keywords: economics, neural networks
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.

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