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Forecasting Exchange Rates with a Large Bayesian VAR

Author

Listed:
  • Andrea Carriero

    (Queen Mary, University of London)

  • George Kapetanios

    (Queen Mary, University of London)

  • Massimiliano Marcellino

    (European University Institute and Bocconi University)

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.

Suggested Citation

  • Andrea Carriero & George Kapetanios & Massimiliano Marcellino, 2008. "Forecasting Exchange Rates with a Large Bayesian VAR," Working Papers 634, Queen Mary University of London, School of Economics and Finance.
  • Handle: RePEc:qmw:qmwecw:634
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    More about this item

    Keywords

    Exchange rates; Forecasting; Bayesian VAR;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • F31 - International Economics - - International Finance - - - Foreign Exchange

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