Exchange rate predictability and dynamic Bayesian learning

Abstract

We consider how an investor in the foreign exchange market can exploit predictive information by means of flexible Bayesian inference. Using a variety of vector autoregressive models, the investor is able, each period, to learn about important data features. The developed methodology synthesizes a wide array of established approaches for modeling exchange rate dynamics. In a thorough investigation of monthly exchange rate predictability for 10 countries, we find that using the proposed methodology for dynamic asset allocation achieves substantial economic gains out of sample. In particular, we find evidence for sparsity, fast model switching, and exploitation of the exchange rate cross-section.