An Empirical Analysis Of Volatility Between Volume And Price Returns In Chinese Stock Market

From establish up to now, Chinese stock market have already had a history of less than twenty years. Whether consider from the quantity of issued stocks, or consider from the volumes in shanghai and Shenzhen stock markets, our stock markets all obtained the substantial development. Along with the Chinese stock markets' development, the local researchers were gradually maturity. In recent years, many foreign and domestic scholars have researched the relationships between returns and volumes systemically. It is very important to research the relationship between volume and prices. The relationship between stock market trading volume and price volatility has always been an important issue in the financial field, for the price-volume relation is an important way not only to know the financial market micro-structure but also to study arbitrary chance or market efficiency. The capital market micro-structure theory also points out that the volatility of the financial asset is relevant closely to information. The volatility of the stock market, in a sense, is a reflection of the stock market assimilating, estimating and making use of the new information flown. The trading volume, acting as a proxy of information flow, conveys a kind of price signal. Therefore, it is very necessary to study the stock market volatility from the trading volume. The price-volume relation has already been used in the technique analysis extensively. So, this paper will study on the price-volume relations systemically to find some theoretical supports.This paper will focus on two main issues: one is the static correlativity and dynamic causality between volume and prices. We use linear regressive mode! and Granger causality test to do this research. The other is the relationship between stock market trading volume and price volatility. We try to investigate the degree to which autoregressive conditional heteroscedasticity (ARCH) in stock returns is explained by the dynamics of trading volume. And we also want to study whether Chinese stock market is in line with suggestions from the Mixture of Distribution Hypothesis. A new GARCH model-An asymmetric Component GARCH-M Model-which can divide market volatility into long run volatility and transitory component is introduced to examine the primary theory of Mixture distribution hypothesis (MDH) on volume-price relationship in Chinese stock market. The purpose of this paper is to do an empirical analysis of the relationship and volatility between volume and price returns by the newest data in Chinese stock market. Based on the analytical results and the comparison with western mature capital market, we try to evaluate the development of Chinese stock market and bring forth some relevant policy recommendations. The paper can be divided into six parts.The first part is a general introduction. It mainly states the necessity and importance of studying volume-price relation and defines the actual and theoretical significance of taking trading volume to interpret volatility.In the second chapter, this paper reviews previous research on the relation between price changes and trading volume in financial markets and take the mature conclusion as the objects of the following empirical tests. We also introduce the primary theory of Mixture distribution hypothesis (MDH) on volume-price relationship.In the third chapter, we analyze the static correlativity and dynamic causality between volume and prices. The data that this article involves is the day data of volume and price. The textual data is from 1993.01.04 to 2006.12.29, among them take 1996.12.16 as the boundary line for two stages of data. This paper will carry on the basic statistics analysis about the rate of return and the volume series. We will carry on the analysis from several aspects such as the scope of selected sample, the statistic characteristic and stability of the volume series and the rate of return series etc., including the processing method to the rate of return and the volume series and the econometrical method, such as ADF examination etc. We will apply the linear regressive model to analysis everyday closing price series and the trading volume series of the Shanghai and Shenzhen stock markets. The result is that the positive relationship is maintained between price returns (or the absolute value of the stock price change) and volume. The changes of price returns are mainly brought by unexpected trading volumes. That is in line with the conclusions of western capital markets. Moreover, we analyze the dynamic causality by means of Granger causality test. In the empirical analysis, the result of Granger Causality test shows that price change is obviously helpful to prediction of volume, and in theoretical explanation, the information represented by volume is also held by equilibrium price. So the information contained in volume is useful to traders' adjustment of expectation of equilibrium price. The forth part introduces the GARCH models and the study framework of MDH at first, then on the basis of which, it designs the empirical method to test MDH-adding different volume in the asymmetric component GARCH-M (Autoregressive conditional heteroskedasticity in-mean) model.In Chapter 5, we use asymmetric Component GARCH-M model to study the relationship between movement of the prices and trading volumes in the Chinese stock market. This part is very important, it can be said that previous researches are all the cushions of this chapter. There were 20 stocks selected from Shanghai and Shenzhen stock market as samples for the study. Based on the samples, we estimated and analyzed two groups of GARCH models: one included trading volume, another did not. We find that ARCH effect can be discovered in our stock markets and this kind of effect can be depicted by the asymmetric component GARCH-M model. When trading volume is included in the variance equation, GARCH effects diminish significantly. We found unexpected trading volume is more relevant in explaining the fluctuation of prices than expected trading volume. It shows that the short - run fluctuation in Chinese stock market is mainly caused by unexpected transactions involving new messages. The volatility of our stock market is asymmetric. Our investigations reveal empirical evidence that the volume data used as an indicator of the flow of information into the market has significant explanatory power for the persistence of volatility. These results are in line with suggestions from the Mixture of Distribution Hypothesis. But our stock market still has some shortages compared with the western maturity capital markets.Finally, on the foundation of analyzing of the real example, draw the main conclusion of this text, propose relevant policy recommendations.We investigate not only the static and dynamic relation between volume and price change, but also the volatility of volume and price change. This structure is more integrate than any other research fruits. The data used in this article is the latest, the sample capacity is big enough, and the contained information is also enough. Based on the study of forefathers, we introduce the asymmetric Component GARCH-M model which is very easy to portray asymmetry fluctuations of securities market to carry on the research of volume-price relationship in security market, and draw the volatility characteristics of Chinese stock market from the new angle. Through the empirical research, we further find the theoretical and actual significance of the research on the volume-price relationship.