Exchange Rate Predictability and Dynamic Bayesian Learning
58 Pages Posted: 15 Nov 2018
Date Written: October 23, 2018
This paper considers how an investor in the foreign exchange market can exploit predictive information by means of flexible Bayesian inference. Using a benchmark vector autoregressive model, the investor is able to revise each period past predictive mistakes and learn about important data features such as parameter instability and model switching. The proposed methodology is specified in order to reflect a wide array of established empirical and theoretical patterns of exchange rates. 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 relative to benchmark strategies. In particular, we find strong evidence for sparsity, fast model switching and exploiting the exchange rate cross-section.
Keywords: Exchange Rates, Bayesian Vector Autoregression, Forecasting, Dynamic Portfolio Allocation, Economic Fundamentals
JEL Classification: C11, D83, F31, G12, G15, G17
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