Study On The Relationship Between The Volume And Price Of Stock Market Which Has Based On The State Transition-Copula Model

With the rapid development of economy, the economic globalization and financial integration have greatly enhanced the interdependence of various financial markets,and the relationship between market and financial assets are becoming more complex. The stock of chinese market has been perfected and matured which has gone through wind storm rain of20years. The relationship between quantity and price of the stock has also presented nonlinear and asymmetry, etc.This paper mainly studies Quantity-Price Relation of Chinese stock market which is based on the markov state transition GARCH model (MRS-GARCH model) and markov state transition-mixed copulas model. Taking advantage of Markov state transition describes the status changing process of stock market price and quantity and we select GARCH model and mixed Copula model, then combine them with Markov state transition model to analysis the SSE Composite Index and Real estate sector Index which is in Chinese stock market.According to the peak fat-tailed features of stock price and quantity the MRS-GARCH model is established which is proved reasonable through goodness-of-fit test. In empirical analysis, MRS-GARCH model is compared with GARCH model by AIC. Then we get a result that MRS-GARCH model is better than GARCH model to describe the relationship between quantity and price of stock market. And according to the parameter estimate, Chinese stock market trading volume has a certain effect in the explanation of price fluctuation, and SSE Composite Index of Chinese stock market and Real estate sectors Index suffer different wave states. Although GARCH model has many advantages in practical application, it can’t characterize asymmetry and tail structure of the relationship between volume and price better. When we study the relationship between volume and price, we must fully considered partial correlation structure and differences between two random variables, In view of this, so this paper uses state transition mixed Copula model to analysis and we estimate the parameters of this model by taking advantage of maximum likelihood estimation method. Finally we test this model through Monte Carlo simulation method, then better results are gotten. Empirical results show that using mixed Copula Markov state transition model to depict Chinese stock market related structure quantity and price is very reasonable, and the model can capture asymmetric, nonlinear and tail structural relationship of volume and price fluctuations in different states, which has greater advantages than MRS-GARCH model.