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.
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Paper provided by European University Institute in its series Economics Working Papers with number
ECO2008/33.
Length: Date of creation: 2008 Date of revision: Handle: RePEc:eui:euiwps:eco2008/33
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Find related papers by JEL classification: C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Bayesian Analysis F31 - International Economics - - International Finance - - - Foreign Exchange
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Zwart, G.J. de & Markwat, T.D. & Swinkels, L. & Dijk, D.J.C. van, 2007.
"The Economic Value of Fundamental and Technical Information in Emerging Currency Markets,"
Research Paper
ERS-2007-096-F&A Revision, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus Uni.
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