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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.
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