Hauzenberger, Niko, Huber, Florian. 2019. Model instability in predictive exchange rate regressions. Journal of Forecasting.

RIS

@ARTICLE{Hauzenberger2019,
title = {Model instability in predictive exchange rate regressions},
author = {Niko Hauzenberger and Florian Huber},
year = {2019},
doi = {https://doi.org/10.1002/for.2620},
url = {https://onlinelibrary.wiley.com/doi/full/10.1002/for.2620},
language = {EN},
journal = {Journal of Forecasting},
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.},
}