Forecasting euro exchange rates: How much does model averaging help?
We analyze the performance of Bayesian model averaged exchange rate forecasts for euro/US dollar, euro/Japanese yen, euro/Swiss franc and euro/British pound rates using weights based on the out-of-sample predictive likelihood. The paper also presents a simple stratified sampling procedure in the spirit of Sala i Martin et alia (2004) to obtain model weights based on predictive accuracy. Our results indicate that accounting explicitly for model uncertainty when constructing predictions of euro exchange rates leads to improvements in predictive accuracy as measured by the mean square forecast error. While the forecasting error of the combined forecast tends to be systematically smaller than that of the individual model that would have been chosen based on predictive accuracy in a test sample, random walk forecasts cannot be beaten significantly in terms of squared forecast errors. Direction of change statistics, on the other hand, are significantly improved by Bayesian model averaging.
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