Study On Characteristics Of Dynamic Evolution Of The Network Structure Of The Stock Market Under Severe Volatility

Shanghai Stock Exchange and Shenzhen Stock Exchange in Shanghai and Shenzhen have been set up in 1990 and 1991. This is a historic moment. China’s modern financial markets began to form precisely from this moment. With the opening of China’s capital market, especially after the opening of Shanghai-Hong Kong Stock Connect program, the links between the mainland Chinese capital market and Hong Kong capital market continues to been strengthen. At the same time,the Chinese capital market went through a bull market in 2015 and a big bear market in 2015.It is really important to study if the mechanism of Shanghai-Hong Kong Stock Connect program could stand the big fluctuations in Chinese capital market and the dynamic evolution of a complex system requires an in-depth study.This paper summarizes the theoretical knowledge of complex network theory and the research status in the application of complex networks theory in complex financial system and make reviews about previous studies. This paper pointed out some shortcomings and defectives in previous studies. Data selected in this paper is daily closing price data of stocks of the Shanghai-Hong Kong Stock Connect program from November 17,2014 to January 27,2016. In order to compare differences in the structure of the networks of bull market and bear market state, this paper divided the whole time window into two stages. The first stage is from November 17,2014 to June 12,2015 which is the stage of a bull market; second stage is from June 12,2015 to January 27,2016 which is stage of a bear market.This article construct directed weighted network of Hong Kong and Shanghai stock market based on the combination of econometric models and complex network theory and characterize the long-term equilibrium relationship based on co-integration relationship between stocks.This article presents a realistic economic concept of the first-order impact matrix, and design evaluation index to evaluate stability of the network. According to this paper, I find that distribution of degree of the network has a characteristic of power-law distribution, and stability of the network in bear market is weaker than the one in bull market. After empirical test, this article did robustness test. The second stage in question is subdivided into slump stage and shock stage. Then the author construct complex networks for each stage, and use the same method to analyze the stability of the networks. Robustness test in this article can also supports this conclusion. Since the network structure is more loose in bull market, the stock markets are more decentralized with less systemic risk; and the network structure in bear market is very tight, the market is not disperse enough, so the systemic risk is greater. The conclusion in this paper challenges the stability analysis of traditional complex network theory. According to the traditional complex network theory, the more closely the network is, the greater the robustness is, but this does not apply to financial networks. The measurement of the entire financial system risk is the degree of decentralization, the higher the dispersion degree is, the smaller systemic risk will be.