The RMB Exchange Rate Forecasting

In July 2005,the People's Bank of China announced that China's implementation of managed floating exchange rate regime based on market supply and demand with reference to a basket of currencies which was the big reform of RMB exchange rate system.China's exchange rate regime has gradually formed a more flexible exchange rate pricing mechanism after the ending of pegging solely to the USD.The volatility and non-linear characteristics of exchange rate are becoming more and more obvious.Uncertainties of exchange rate changes bring challenges to trade decision-making,and affect the formulation and adjustment of relevant policies such as exchange rate policy and interest rate policy.Forecasting RMB exchange rate accurately becomes increasingly important but difficult in the meantime.Most traditional statistical model models and linear forecasting methods perform less well in the new economic environment.While the non-parametric exchange rate forecasting method,which is represented by artificial neural network,has achieved good results in the RMB exchange rate forecasting.However,the theoretical basis and the choice of model structure have limitations which lead to over-fitting and poor forecasting results of out-of-sample data of RMB exchange rate in practice.Therefore,to overcome the shortcomings of the previous exchange rate forecasting model,it is of great practically importance to construct a RMB exchange rate forecasting model in the more open environment based on the exchange rate determination theory,which is the core significance and innovation of the paper.Based on the theory of statistic learning and minimum structure risk principle,support vector machine(SVM)method has a more complete theoretical basis,and it is widely used in financial time series forecasting field.Based on the previous study of exchange rate forecasting,this paper combines the exchange rate determination theory with the support vector machine(SVM)method.By reconstructing the exchange rate index system,the autoregressive model and the BP neural network model are used as the control models to evaluate and analyze the RMB exchange rate forecasting results.The paper is divided into five parts.In the first part,it combs the previous literatures and research results,briefly introduces the content structure and innovation of the article.The second part elaborates the basic theory of exchange rate determination theory and support vector machine(SVM),which is intended to find the theoretical basis for exchange rate forecasting.Then in the third part,it mainly introduces the steps of the exchange rate forecasting model construction.The fourth part is the empirical part of this paper which analyzes the exchange rate of the RMB against the US dollar by applying the exchange rate forecasting model based on the support vector machine(SVM)method.The last part summarizes the method of forecasting the exchange rate based on support vector machine(SVM)and research of the method is discussed.Comparative analysis are made from the perspective of the overall error and stability of the forecasting error using autoregressive and BP neural network as control models.The comparison results show that the method based on support vector machine is not only better than the control models in forecasting accuracy and stability,but also more comprehensive in the theoretical basis of the model,which contains the economic factors reflected by exchange rate behavior.