The Research On The Renminbi Exchange Rate Forecasting By Artificial Nutural Networks

In an open economy, as a key variable of the economic systerm, exchange rate not only adjust and associate with various macro-and micro-economic factors, but also impact the balance of internal and external economies. RMB exchange rate system is the lever, which regulates internal and external balance of China's national economy, and the important bridge to maintain economic ties between China and other countries as well. Especially after the exchange rate reform implemented from July 21, 2005, RMB exchange rate became the key issues which influences the world economic situation and trade relations between China and its important trading partners, such as the USA and the EU. Therefore, it has great theoretical significance and application value to explore the inherent laws of the system and capture its characteristic for accurately predicting the RMB exchange rate.The complexity of the exchange rate behavior calls for new normal formulas when making prediction study. The Artificial Neural Network technique is an effective tool for approximating and modeling nonlinear system. It has good nonlinear mapping capability, adaptive, self-learning and generalization ability. Especially, the dynamic neural network, which containing feedback process can directly and vividly reflect the dynamic nature of systems, thus it is attractive to use neural network technology to forecast the dynamic, non-linear exchange rate.In this paper, firstly we reviews and commentates the literatures of foreign exchange rate forecast, then introduces and analyzes from three perspectives, which are the basic theory of the exchange rate forecast, the main model and technical approach. Then a brief introduction was given on the artificial neural network technology, including its development, features and the theory. In the next part, first the concepts of generalization and the over-fitting issue in neural networks training process were introduced, then the author discusses some issues on the key parameters in modeling the neural network both from itself and the training process, according to the reason why over-fitting is caused. At last, some evaluation standards of forecasting performance were introduced.In the empirical studies part, the author first tests the non-linear features of the RMB exchange rate from four aspects, and finds that both the RMB/USD and the RMB/EUR exchange rate have complex nonlinear dynamic features. So the neural network, as one of the non-linear based approach, can be used for the fitting and forecasting. Then, from aspects such as optimal lag period and the best samples number of training collection, the author estimates the key parameters impacting the forecasting ability of neural network model. We adopted most used feed forward network MLP model and three kinds of basic dynamic feedback neural network model to train and forecast the time series of RMB exchange rate.By comparing the neural network models under different freedom parameters and the simple random walk model, we identify and select the optimal neural network model for each RMB exchange rate. The results showed in general that the recurrent network models is with beter performance in both in-sample fit and out-of-sample forcast on each RMB exchange rate time series. And for each ANN model, there is no direct relationship between the performance of in-sample fit and out-of-sample forcast.The accumulation in details proved that RNN2(1) and RNN3(1) are the best forecasting model respectively on the RMB/USD price level in four weeks and the fluctuations in the first week, RNN2(1) and RNN1(1) are the best forecasting model respectively on the RMB/EUR price level in four weeks and the fluctuations in the first week and the second week respectively. The overall predictive ability is superior to other models significantly. The conclusion proves the hypothesis that the prediction capability of different ANN model relies on different time series of exchange rate.In all, the paper has made some innovation both in the theoretical analysis and in the empirical application part. The study make an improvement in accurately predicting both the price level and the fluctuation changes of RMB exchange rate, which further have some practical usefulness for the Central Bank to formulate effective foreign exchange intervention and monetary policies, or for the corporate to avoid foreign exchange risk, etc.