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

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.