The Application of Artificial Neural Networks to Exchange Rate Forecasting: The Role of Market Microstructure Variables
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|Length:||36 pages Abstract: Artificial neural networks (ANN) are employed for high-frequency Canada/U.S. dollar exchange rate forecasting. ANN outperform random walk and linear models in a number of recursive out-of- sample forecasts. The inclusion of a microstructure variable, order flow, substantially improves the predictive power of both the linear and non-linear models. Two criteria are applied to evaluate model performance: root-mean squared error (RMSE) and the ability to predict the direction of exchange rate moves. ANN is consistently better in RMSE than 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 (PERC). The empirical results suggest that optimal ANN architecture is superior to random walk and any linear competing model for high-frequency exchange rate forecasting.|
|Date of creation:||2000|
|Contact details of provider:|| Postal: 234 Wellington Street, Ottawa, Ontario, K1A 0G9, Canada|
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- Hsieh, David A, 1989. "Testing for Nonlinear Dependence in Daily Foreign Exchange Rates," The Journal of Business, University of Chicago Press, vol. 62(3), pages 339-368, July.
- Kaashoek, J.F. & van Dijk, H.K., 1999. "Neural network analysis of varying trends in real exchange rates," Econometric Institute Research Papers EI 9915-/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
- Amano, Robert A. & van Norden, Simon, 1995. "Terms of trade and real exchange rates: the Canadian evidence," Journal of International Money and Finance, Elsevier, vol. 14(1), pages 83-104, February.
- Zhang, Gioqinang & Hu, Michael Y., 1998. "Neural network forecasting of the British Pound/US Dollar exchange rate," Omega, Elsevier, vol. 26(4), pages 495-506, August.
- Joseph Plasmans & William Verkooijen & Hennie Daniels, 1998. "Estimating structural exchange rate models by artificial neural networks," Applied Financial Economics, Taylor & Francis Journals, vol. 8(5), pages 541-551.
- Kuan, Chung-Ming & Liu, Tung, 1995. "Forecasting Exchange Rates Using Feedforward and Recurrent Neural Networks," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 10(4), pages 347-364, Oct.-Dec..
- Meese, Richard A. & Rogoff, Kenneth, 1983. "Empirical exchange rate models of the seventies : Do they fit out of sample?," Journal of International Economics, Elsevier, vol. 14(1-2), pages 3-24, February.
- 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.
- Meese, Richard A & Rose, Andrew K, 1990. "Nonlinear, Nonparametric, Nonessential Exchange Rate Estimation," American Economic Review, American Economic Association, vol. 80(2), pages 192-196, May.
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