Abstract: In this paper, we discuss a Bayesian approach to
quantile autoregressive (QAR) time series model estimation and
forecasting. Together with a combining forecasts technique, we then
predict USD to GBP currency exchange rates. Combined forecasts
contain all the information captured by the fitted QAR models
at different quantile levels and are therefore better than those
obtained from individual models. Our results show that an unequally
weighted combining method performs better than other forecasting
methodology. We found that a median AR model can perform well in
point forecasting when the predictive density functions are symmetric.
However, in practice, using the median AR model alone may involve
the loss of information about the data captured by other QAR models.
We recommend that combined forecasts should be used whenever
possible.