State Space Model And Its Application On The Shanghai Stock Market

In the past several years, Chinese stock market had a rapidly development. Stocks have played a very important role in the people's life and the national economy. The analyses of the stock markets become very important not only to the investor but also to the manager. In this paper, the author wants to find an accurate and simplest method by combing the statistic and time series knowledge. State space model embodies the affect factors by using a state vector; the stock index can be expressed by the linear combination of the state vector. This model involves ARIMA model, and become a dynamic recursive method combined with Kalman filter. After analyzing the Shanghai A,B stock index we find that the state space model combined with Kalman filter is a better model. This paper has three chapters. In the first chapter Chinese stock market and stock index are introduced, also we analyze the affect factors. In the second chapter we give the knowledge of state space and discuss the relationship of state space model and ARIMA model, the Kalman filter is also introduced in this chapter. In the last chapter we analyze the stock index by using SAS system, after comparing the errors of different models we find that the state space model combined with the Kalman filter is an efficient model.