Throughout the past 3 decades, the random walk model served as exchange rate forecasting benchmark to verify that a model is able to outperform a random process. However, its application as forecasting benchmark is contradictory. Rather than serving as a benchmark that explains exchange rate behaviour, it serves as a benchmark of what we do not understand in exchange rate forecasting – the random component. In order to accommodate for the observed mean reverting and non-linear patterns in exchange rate information, this study considers various univariate models to serve as linear or non-linear benchmarks of exchange rate forecasting. The results of forecasting performance indicate that the random walk model is an insufficient benchmark to explain exchange rate movements for non-static models. As linear alternative, an autoregressive model performed best to explain the mean reverting patterns in exchange rate information for quarterly, monthly and weekly forecasts of the exchange rate. As non-linear alternative, a Kernel regression was best able to explain volatile exchange rate movements associated with daily forecasts of the exchange rate.