Forecasting Exchange Rates with a Large Bayesian VAR

Andrea Carriero (), George Kapetanios and Massimiliano Marcellino

No 7008, CEPR Discussion Papers from C.E.P.R. Discussion Papers

Abstract: Models based on economic theory have serious problems at 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, the use of 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 the 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 any forecast horizon, including at 1-step ahead.

Keywords: Bayesian VAR; Exchange Rates; Forecasting (search for similar items in EconPapers)
JEL-codes: C11 C53 F31 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cba and nep-for
Date: 2008-10
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Related works:
Working Paper: Forecasting Exchange Rates with a Large Bayesian VAR (2008)
Working Paper: Forecasting Exchange Rates with a Large Bayesian VAR (2008)
Journal Article: Forecasting exchange rates with a large Bayesian VAR (2009)
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