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Forecasting exchange rates with a large Bayesian VAR

Andrea Carriero (), G. Kapetanios and Massimiliano Marcellino

International Journal of Forecasting, 2009, vol. 25, issue 2, 400-417

Abstract: Models based on economic theory have serious problems forecasting exchange rates better than simple univariate driftless random walk models, especially at short horizons. Multivariate time series models suffer from the same problem. In this paper, we propose to forecast exchange rates with a large Bayesian VAR (BVAR), using a panel of 33 exchange rates vis-a-vis the US Dollar. Since exchange rates tend to co-move, a large set of them can contain useful information for forecasting. In addition, we adopt a driftless random walk prior, so that cross-dynamics matter for forecasting only if there is strong evidence of them in the data. We produce forecasts for all 33 exchange rates in the panel, and show that our model produces systematically better forecasts than a random walk for most of the countries, and at all forecast horizons, including 1-step-ahead.

Keywords: Exchange; rates; Forecasting; Bayesian; VAR (search for similar items in EconPapers)
Date: 2009
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Related works:
Working Paper: Forecasting Exchange Rates with a Large Bayesian VAR (2008) Downloads
Working Paper: Forecasting Exchange Rates with a Large Bayesian VAR (2008) Downloads
Working Paper: Forecasting Exchange Rates with a Large Bayesian VAR (2008) Downloads
Working Paper: Forecasting Exchange Rates with a Large Bayesian VAR (2008) Downloads
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