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)
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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 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 |
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Status: | Published |
Refereed: | Yes |
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 |
Publisher: | Wiley |
ISSN: | 0883-7252 |
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 |
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