Abstract

Literature shows that exchange rates are largely unpredictable, and that a simple random walk outperforms structural exchange rate models. In order to determine whether fundamentals explain exchange rate behaviour in South Africa, the two approaches to exchange rate forecasting - the technical and fundamental approach - will be compared. Various univariate time series models, including the random walk model, will be compared to various multivariate time series models (using the MAD/mean ratio), combining the two approaches. The determinants of the South African exchange rate are identified, and these determinants are used to specify multivariate time series models for the South African exchange rate. The multivariate models (VARMA) outperformed the univariate models (except for the Random walk model) in the short-run forecasts, one step ahead, while the multivariate models, performed better in the longer-run forecasts. To improve the accuracy of especially the multivariate models, it is recommended that multiple frequencies be used to capture the dynamic behaviour between variables in a Structural VAR framework.

Key words: Evaluation forecasts, cointegration, error correction models, ARIMA models, VAR models, VARMA models.