Abstract:
Traditionally, the literature on forecasting exchange rates with many potential predictors have primarily only
accounted for parameter uncertainty using Bayesian Model Averaging (BMA). Though BMA-based models
of exchange rates tend to outperform the random walk model, we show that when accounting for model
uncertainty over and above parameter uncertainty through the use of Dynamic model Averaging (DMA), the
gains relative to the random walk model are even bigger. That is, DMA models outperform not only the
random walk model, but also the BMA model of exchange rates. We obtain these results based on fifteen
potential predictors used to forecast two South African Rand-based exchange rates. In the process, we also
unveil variables, which tends to vary over time, that are good predictors of the Rand-Dollar and Rand-Pound
exchange rates at different forecasting horizons.
