Dynamic Analysis Of The Relationship Between The Fluctuations In The Chinese Stock Market Industries

The Shanghai stock exchange and the Shenzhen stock exchange has been established for twenty years since 1990. After 20 years’ development, China’s stock market is playing a more and more important role in China’s economy. In the stock market, the mutual influence between industry and industry is becoming more and more obvious. An industry’s fluctuation often leads to another industry’s fluctuation, i.e. in the stock market, volatility between industries has obvious linkage effects of volatility. In order to further analyze the interaction mechanism and the dynamic interaction between the volatility of the industry in the stock market, this paper selects the agricultural, manufacturing, financial services and real estate industry four industries as the representatives, and then studies the dynamic relationship between industries’ volatility in-depth. In this paper, the concrete application of the econometric method includes correlation test, Granger causality test, impulse response analysis, unit GARCH models, and multivariate GARCH model of the dynamic correlation coefficients (namely DCC-MGARCH model). This article is expected to give the broad masses of investors and managers to inspiration. When great changes have taken place in the stock market volatility, investors can consider the index of the dynamic conditional correlation coefficient which can better reflect the stock market fluctuation correlation between different industries. And then investors can have an in-depth understanding of relevant property of market fluctuations and volatility between industries, thus make more informed investment decision, and improve the ability to avoid risk.The thesis is mainly divided into seven parts. First of all, the introduction part is the selected topic background and research significance of this article. Then, in the first part I introduce the theoretical basis of the thesis. The basic theory mainly includes the efficient market theory, the definition of volatility between industries, and the mechanism of volatility effects between industries,as well as the factors influencing the stock market volatility. At last of the first part, the article introduces the model theory, mainly including the ARCH model, single variable GARCH model and multivariate GARCH model GARCH model, and granger causality test. In the multivariate GARCH model the article focuses on the introduction of the dynamic conditional correlation multivariate GARCH model (namely the DCC-MGARCH model). In the second part, this article respectively discusses the industries’volatility characteristics of China’s stock market. The third part and the fourth part is the core part of this paper, the empirical part. In the third part, firstly the article processes the industry data and makes statistic description; then the article makes the stationary test and correlation test between variables; finally, the article makes the granger causality test and impulse response analysis. In the fourth part, the article begins to establish DCC-MGARCH model. The results of the empirical analysis found that:agricultural, manufacturing, financial services and real estate industry have shown a positive dynamic correlation among the four industries’ returns volatility. The fluctuation’s dynamic correlation between agricultural and manufacturing is strongest; the fluctuation’s dynamic correlation between agricultural and real estate industry, manufacturing industry and financial service industry, manufacturing industry and real estate industry, financial services and real estate seconds; the fluctuation’s dynamic correlation between agricultural and financial services compared to other industry’s fluctuation linkage is minimum. The last two parts of this paper is the conclusion, enlightenment and prospect. In this part, this paper points out the limitations of the study and follow-up research ideas.