Exchange rate predictability and dynamic Bayesian learning

Beckkmann, J., Koop, G., Korobilis, D. and Schuessler, R. A. (2020) Exchange rate predictability and dynamic Bayesian learning. Journal of Applied Econometrics, 35(4), pp. 410-421. (doi: 10.1002/jae.2761)

[img] Text
223799.pdf - Published Version
Available under License Creative Commons Attribution.



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 modelling exchange rate dynamics. In a thorough investigation of monthly exchange rate predictability for ten 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 exploiting the exchange rate cross-section.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Korompilis Magkas, Professor Dimitris
Authors: Beckkmann, J., Koop, G., Korobilis, D., and Schuessler, R. A.
Subjects:H Social Sciences > HG Finance
College/School:College of Social Sciences > Adam Smith Business School > Economics
Journal Name:Journal of Applied Econometrics
ISSN (Online):1099-1255
Published Online:01 June 2020
Copyright Holders:Copyright © 2020 The Authors
First Published:First published in Journal of Applied Econometrics 35(4):410-421
Publisher Policy:Reproduced under a Creative Commons License

University Staff: Request a correction | Enlighten Editors: Update this record