Predicting Exchange Rates Out of Sample: Can Economic Fundamentals Beat the Random Walk?

Jiahan Li, Ilias Tsiakas and Wei Wang

Journal of Financial Econometrics, 2015, vol. 13, issue 2, 293-341

Abstract: This article shows that economic fundamentals can generate reliable out-of-sample forecasts for exchange rates when prediction is based on a “kitchen-sink” regression that incorporates multiple predictors. The key to establishing predictability is estimating the kitchen-sink regression with the elastic-net shrinkage method, which improves performance by reducing the effect of less informative predictors in out-of-sample forecasting. Using statistical and economic measures of predictability, we show that our approach outperforms alternative models, including the random walk, individual exchange rate models, a kitchen-sink regression estimated with ordinary least squares, standard forecast combinations, and popular ad-hoc strategies such as momentum and the 1/N strategy.

Keywords: exchange rates; out-of-sample forecasting; elastic net; combined forecasts (search for similar items in EconPapers)
JEL-codes: F31 F37 G11 G15 G17 (search for similar items in EconPapers)
Date: 2015
References: View complete reference list from CitEc
Citations: View citations in EconPapers (44)

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Working Paper: Predicting Exchange Rates Out of Sample: Can Economic Fundamentals Beat the Random Walk? (2014)
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