A hybrid model for exchange rate prediction

Exchange rate forecasting is an important problem. Several forecasting techniques have been proposed in order to gain some advantages. Most of them are either as good as random walk forecasting models or slightly worse. Some researchers argued that this shows the efficiency of the exchange market. We propose a two stage forecasting model which incorporates parametric techniques such as autoregressive integrated moving average (ARIMA), vector autoregressive (VAR) and co-integration techniques, and nonparametric techniques such as support vector regression (SVR) and artificial neural networks (ANN). Comparison of these models showed that input selection is very important. Furthermore, our findings show that the SVR technique outperforms the ANN for two input selection methods.