Model instability in predictive exchange rate regressions

Hauzenberger, Niko and Huber, Florian (2020) Model instability in predictive exchange rate regressions. Journal of Forecasting, 39 (2). pp. 168-186. ISSN 0277-6693 (https://doi.org/10.1002/for.2620)

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Abstract

In this paper we aim to improve existing empirical exchange rate models by accounting for uncertainty with respect to the underlying structural representation. Within a flexible Bayesian framework, our modeling approach assumes that different regimes are characterized by commonly used structural exchange rate models, with transitions across regimes being driven by a Markov process. We assume a time-varying transition probability matrix with transition probabilities depending on a measure of the monetary policy stance of the central bank at home and in the USA. We apply this model to a set of eight exchange rates against the US dollar. In a forecasting exercise, we show that model evidence varies over time, and a model approach that takes this empirical evidence seriously yields more accurate density forecasts for most currency pairs considered.

ORCID iDs

Hauzenberger, Niko ORCID logoORCID: https://orcid.org/0000-0002-2683-8421 and Huber, Florian;
CORE (COnnecting REpositories)