Forecasting exchange rates using TSMARS

In this article we use the Time Series Multivariate Adaptive Regression Splines (TSMARS) methodology to estimate and forecast non-linear structure in weekly exchange rates for four major currencies during the 1980s. The methodology is applied in three steps. First, univariate models are fitted to the data and the residuals are checked for outliers. Since significant outliers are spotted in all four currencies, the TSMARS methodology is reapplied in the second step with dummy variables representing the outliers. The empirical residuals of the models obtained in the second step pass the standard diagnostic tests for non-linearity, Gaussianity and randomness. Moreover, the estimated models can be sensibly interpreted from an economic standpoint. The out-of-sample forecasts generated by the TSMARS models are compared with those obtained from a pure random walk. We find that for two of the currencies, the models obtained using TSMARS provide forecasts which are superior to those of a random walk at all forecast horizons.