Analyzing RMB Real Ecxhange Rate Behavior Based On Radial Basis Function

Exchange rate has an important effect on macro-economy. It is an important variable of financial market. After the breakdown of the Bretton Woods System, floating exchange rate system has become the main exchange rate system in the world. Exchange rate fluctuates frequently, rising and falling uncertainly. Exchange rate risk became one of most important financial risks. It has been given much attention and all countries and many companies have take many measures in order to make the best of exchange rate fluctuation. So, the analysis between exchange rate and other macroeconomic variables and behavior description and forecasting of exchange rate based on it are attractive in financial field. With the development of computer technology and econometrics, new ways and tools have been used to study about exchange rate fluctuation. This paper uses the cointegration analysis technology which has been widely used from 80's of last century and neural network to describe exchange rate behavior and forecast it. Compared with the other economical estimate methods, the new network model is novel, has better forecast precision and effectiveness and provides the theoretical reference for the execution of managed floating exchange rate.The paper was divided into four chapters, the first chapter outlines the achievement of describing exchange rate behavior, has comprehensively understood and grasped the front trend of this question research, has built a better foundation for developing this paper smoothly. The second chapter systematically narrates classics exchange rate theories and corresponding models, and carries on simple appraisal to each theory, Then, the paper analyzes the present RMB exchange rate system characteristic, analyzes the shortnesses of the traditional exchange rate behavior describing. And summaries the basic economical essential variable about RMB exchange rate. The third chapter expatiates the theory and concretely technology about cointegrationt. Based on this, the paper puts forward the correctly steps to describe exchange rate and forecast it. Based on those, the fourth chapter introduces the artificial neural network technology and establishes the radial basis function network model, The paper tests the predication results with the same error indexes, and make some comparisons and evaluations between two models. Demonstrantion results have proved that radial basis function network is a good way to describe and forecast exchange rate and neural network can catch the nonlinear factors of exchange rate...