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Model instability in predictive exchange rate regressions.

Niko Hauzenberger1,2, Florian Huber2

  • 1Department of Economics WU Vienna University of Economics and Business Vienna Austria.

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Summary
This summary is machine-generated.

This study enhances exchange rate models by incorporating structural uncertainty using a Bayesian framework. Accounting for time-varying monetary policy improves density forecasts for most US dollar exchange rates.

Keywords:
Markov switchingempirical exchange rate modelsexchange rate fundamentals

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Area of Science:

  • Economics
  • Econometrics
  • Computational Finance

Background:

  • Existing empirical exchange rate models often fail to account for underlying structural uncertainties.
  • Accurate exchange rate forecasting is crucial for international finance and monetary policy.

Purpose of the Study:

  • To improve empirical exchange rate models by integrating uncertainty in structural representations.
  • To develop a flexible Bayesian framework that captures regime shifts driven by monetary policy.

Main Methods:

  • A Bayesian regime-switching model with a time-varying transition probability matrix.
  • Transition probabilities are linked to the monetary policy stance of domestic and US central banks.
  • The model is applied to eight exchange rates against the US dollar.

Main Results:

  • Model evidence fluctuates over time, indicating the dynamic nature of exchange rate determinants.
  • The proposed model demonstrates superior density forecast accuracy for most considered currency pairs.
  • Incorporating monetary policy stance significantly impacts transition probabilities between regimes.

Conclusions:

  • Acknowledge and model structural uncertainty for more robust exchange rate predictions.
  • Monetary policy plays a key role in driving exchange rate dynamics through regime shifts.
  • The Bayesian regime-switching approach offers a valuable tool for exchange rate modeling and forecasting.