Out-of-Sample Exchange Rate Prediction: A Machine Learning Perspective

66 Pages Posted: 27 Sep 2019 Last revised: 6 Apr 2022

See all articles by Ilias Filippou

Ilias Filippou

Washington University in St. Louis - John M. Olin Business School

David Rapach

Research Department, Federal Reserve Bank of Atlanta; Washington University in St. Louis

Mark P. Taylor

Washington University in St. Louis - John M. Olin Business School; Centre for Economic Policy Research (CEPR); Brookings Institution

Guofu Zhou

Washington University in St. Louis - John M. Olin Business School

Date Written: April 5, 2022

Abstract

We establish the out-of-sample predictability of monthly exchange rates via machine learning techniques based on 70 predictors capturing country characteristics, global variables, and their interactions. To better guard against overfitting in our high-dimensional and noisy data environment, we adjust “off-the-shelf” implementations of machine learning techniques to induce adequate shrinkage. The resulting forecasts consistently outperform the no-change benchmark, which has proven difficult to beat. Variable importance analysis indicates that country characteristics are important for forecasting, once they interact with global variables. Machine learning forecasts also markedly improve the performance of a carry trade portfolio, especially since the Global Financial Crisis.

Keywords: Short-horizon exchange rate predictability, Panel predictive regression, Elastic net, Deep neural network, Variable importance, Partial dependence plot, Individual conditional expectation curves, Carry trade

JEL Classification: C45, F31, F37, G11, G12, G15

Suggested Citation

Filippou, Ilias and Rapach, David and Taylor, Mark P. and Zhou, Guofu, Out-of-Sample Exchange Rate Prediction: A Machine Learning Perspective (April 5, 2022). Available at SSRN: https://ssrn.com/abstract=3455713 or http://dx.doi.org/10.2139/ssrn.3455713

Ilias Filippou

Washington University in St. Louis - John M. Olin Business School ( email )

One Brookings Drive
Campus Box 1133
St. Louis, MO 63130-4899
United States

David Rapach (Contact Author)

Research Department, Federal Reserve Bank of Atlanta ( email )

1000 Peachtree Street N.E.
Atlanta, GA 30309-4470
United States

Washington University in St. Louis

One Brookings Drive
Campus Box 1133
St. Louis, MO 63130-4899
United States

HOME PAGE: http://https://sites.google.com/slu.edu/daverapach

Mark P. Taylor

Washington University in St. Louis - John M. Olin Business School ( email )

One Brookings Drive
Campus Box 1156
St. Louis, MO 63130-4899
United States

Centre for Economic Policy Research (CEPR)

London
United Kingdom

Brookings Institution ( email )

1775 Massachusetts Ave, NW
Washington, DC 20036
United States

Guofu Zhou

Washington University in St. Louis - John M. Olin Business School ( email )

Washington University
Campus Box 1133
St. Louis, MO 63130-4899
United States
314-935-6384 (Phone)
314-658-6359 (Fax)

HOME PAGE: http://apps.olin.wustl.edu/faculty/zhou/

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