Can We Beat the Random-Walk Model for the South African Rand–U.S. Dollar and South African Rand–UK Pound Exchange Rates? Evidence from Dynamic Model Averaging

Traditionally, the literature on forecasting exchange rates with many potential predictors has primarily only accounted for parameter uncertainty using Bayesian model averaging (BMA). Although 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) and dynamic model selection (DMS), the gains relative to the random-walk model are even bigger. That is, DMA and DMS models outperform not only the random-walk model, but also the BMA model of exchange rates. Furthermore, sensitivity analysis reveals that in exchange-rate modeling, accounting for parameter uncertainty may even be more important than parameter uncertainty. Our results are based on fifteen potential predictors used to forecast two South African rand–based exchange rates. We also unveil variables, which tend to vary over time, that are good predictors of the rand–dollar and rand–pound exchange rates at different forecasting horizons.