Exchange Rate Predictability and Dynamic Bayesian Learning

This paper considers how an investor in the foreign exchange market can exploit predictive information by means of flexible Bayesian inference. Using a variety of different vector autoregressive models, the investor is able, each period, to revise past predictive mistakes and learn about important data features. The proposed methodology is developed in order to synthesize a wide array of established approaches for modelling exchange rate dynamics. In a thorough investigation of monthly exchange rate predictability for ten countries, we find that an investor using the proposed flexible methodology for dynamic asset allocation achieves significant economic gains out of sample relative to benchmark strategies. In particular, we find strong evidence for sparsity, fast model switching and exploiting the exchange rate cross-section