Exchange rate predictability and dynamic Bayesian learning

Joscha Beckmann, Gary Koop, Dimitris Korobilis, Rainer Alexander Schüssler

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)
13 Downloads (Pure)


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.

Original languageEnglish
Pages (from-to)410-421
Number of pages12
JournalJournal of Applied Econometrics
Issue number4
Early online date16 Apr 2020
Publication statusPublished - 30 Jun 2020


  • exchange rates
  • Bayesian Vector Autoregression
  • forecasting
  • dynamic portfolio allocation
  • economic fundamentals


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