Exchange Rate Forecasting with Advanced Machine Learning Methods

Jonathan Felix Pfahler
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Jonathan Felix Pfahler: Department of Statistics, University of Augsburg, Universitaetstr. 16, D-86159 Augsburg, Germany

JRFM, 2021, vol. 15, issue 1, 1-17

Abstract: Historically, exchange rate forecasting models have exhibited poor out-of-sample performances and were inferior to the random walk model. Monthly panel data from 1973 to 2014 for ten currency pairs of OECD countries are used to make out-of sample forecasts with artificial neural networks and XGBoost models. Most approaches show significant and substantial predictive power in directional forecasts. Moreover, the evidence suggests that information regarding prediction timing is a key component in the forecasting performance.

Keywords: machine learning; exchange rate forecasting; fundamentals (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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