Exchange rate predictability and dynamic Bayesian learning

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

Journal of Applied Econometrics, 2020, vol. 35, issue 4, 410-421

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.

Date: 2020
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Citations: View citations in EconPapers (19)

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https://doi.org/10.1002/jae.2761

Related works:
Working Paper: Exchange rate predictability and dynamic Bayesian learning (2018)
Working Paper: Exchange rate predictability and dynamic Bayesian learning (2017)
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