Exchange rate predictability and dynamic Bayesian learning

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Beckmann, Joscha and Koop, Gary and Korobilis, Dimitris and Schüssler, Rainer Alexander (2020) Exchange rate predictability and dynamic Bayesian learning. Journal of Applied Econometrics, 35 (4). pp. 410-421. ISSN 0883-7252 (https://doi.org/10.1002/jae.2761)

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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.

ORCID iDs

Beckmann, Joscha, Koop, Gary ORCID logoORCID: https://orcid.org/0000-0002-6091-378X, Korobilis, Dimitris and Schüssler, Rainer Alexander;
  • Item type:Article
    ID code:71256
    Dates:
    Date
    Event
    30 June 2020
    Published
    16 April 2020
    Published Online
    24 January 2020
    Accepted
    Subjects:Social Sciences > Economic Theory
    Department:Strathclyde Business School > Economics
    Depositing user: Pure Administrator
    Date deposited:28 Jan 2020 16:47
    Last modified:04 Dec 2024 08:05
    URI:https://strathprints.strath.ac.uk/id/eprint/71256
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