Model specification and forecasting foreign exchange rates with vector autoregressions

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Abstract

This study examines the forecasting accuracy of alternative vector autoregressive models each in a seven-variable system that comprises in turn of daily, weekly and monthly foreign exchange (FX) spot rates. The vector autoregressions (VARs) are in non-stationary, stationary and error-correction forms and are estimated using OLS. The imposition of Bayesian priors in the OLS estimations also allowed us to obtain another set of results. We find that there is some tendency for the Bayesian estimation method to generate superior forecast measures relatively to the OLS method. This result holds whether or not the data sets contain outliers. Also, the best forecasts under the non-stationary specification outperformed those of the stationary and error-correction specifications, particularly at long forecast horizons, while the best forecasts under the stationary and error-correction specifications are generally similar. The findings for the OLS forecasts are consistent with recent simulation results. The predictive ability of the VARs is very weak. Copyright © 2001 John Wiley & Sons, Ltd.
Original languageEnglish
Pages (from-to)451-484
Number of pages34
JournalJournal of Forecasting
Volume20
Issue number7
DOIs
Publication statusPublished - 9 Nov 2001
Externally publishedYes

Keywords

  • Bayesian estimation, outliers
  • Cointegration
  • Error-correction models
  • Forecasting
  • Seasonality
  • Vector autoregressions

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