Evolutionary support vector machine for RMB exchange rate forecasting

The volatility of exchange rate is very important to a country's trading. Accurately forecasting exchange rate time series appears to be a challenging task for the scientific community on account of its nonstationary and nonlinear structural nature. In order to improve the performance of exchange rate forecasting, this study develops two evolutionary support vector regression models to forecast four typical RMB exchange rates (CNY against USD, EUR, JPY and GBP), and employs four evaluation criteria to assess the performance of out-of-sample exchange rate forecasting. In this study, the evolutionary algorithm optimizes the SVR parameters by balancing search between the global and local optima. However, the inputs of models are selected though phase space reconstruction method from historical data of exchange rate series. The empirical results demonstrate that our proposed evolutionary support vector regression outperforms all other benchmark models in terms of level forecasting accuracy, directional forecasting accuracy and statistical accuracy. As it turns out, our proposed evolutionary support vector regression is a promising approach for RMB exchange rate forecasting.