Exchange rate predictability and dynamic Bayesian learning

Rainer Schüssler, Joscha Beckmann, Gary Koop and Dimitris Korobilis

VfS Annual Conference 2018 (Freiburg, Breisgau): Digital Economy from Verein für Socialpolitik / German Economic Association

Abstract: This paper considers how an investor in foreign exchange markets might exploit predictive information in macroeconomic fundamentals by allowing for switching between multivariate time series regression models. These models are chosen to reflect a wide array of established empirical and theoretical stylized facts. In an application involving monthly exchange rates for seven countries, we find that an investor using our methods to dynamically allocate assets achieves significant gains relative to benchmark strategies. In particular, we find strong evidence for fast model switching, with most of the time only a small set of macroeconomic fundamentals being relevant for forecasting.

Keywords: Exchange rates; economic fundamentals; Bayesian vector autoregression; forecasting; dynamic portfolio allocation (search for similar items in EconPapers)
JEL-codes: C11 D83 F31 G12 G15 G17 (search for similar items in EconPapers)
Date: 2018
New Economics Papers: this item is included in nep-for and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Related works:
Journal Article: Exchange rate predictability and dynamic Bayesian learning (2020)
Working Paper: Exchange rate predictability and dynamic Bayesian learning (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:vfsc18:181523

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