A Bayesian vector error correction model for forecasting exchange rates

Abstract

This paper develops a new method called Bayesian Vector Error Correction Model (BVECM), which is applied to forecast 1 month ahead changes of currency exchange rates for three major Asia Pacific economies. The study also compares out-of-sample forecasting performance with those of the random walk model and the Bayesian Vector Autoregression (BVAR), which has been shown in recent studies to outperform a variety competing of econometric techniques in exchange rate forecasting. Our experimental results indicate that both BVECM and BVAR are able to forecast the changes in exchange rates better than the random walk model. In terms of conventional forecast evaluation statistics, BVECM outperforms BVAR for all three currencies examined. In addition, the bias tests find that BVECM produces systematically less biased and more efficient out-of-sample forecasts than BVAR. Although the results of market timing tests indicate that both BVAR and BVECM have an economically significant value in predicting the directional change in two of the three exchange rates, BVECM is shown to produce equally or more economically significant directional change forecasts than BVAR.