| In the process of global integration,all-round cooperation has become the inevitable trend of the world economy,and the economic development of various industries has shown the characteristics of interlocking and close links.As the barometer of the national economy,the securities market has become increasingly difficult to manage,and the security risks are becoming more and more obvious.Sudden events in an industry can lead to sharp fluctuations in share prices across industries.In this situation,the research on the correlation between the volatility of stock exchange rate of various trades can improve the security degree of securities market and reduce the risk.This paper,by using more power variation method and the complex network theory to study the internal structure of China's stock market share price jump part,and do the following: Combining the theory of realized volatility and MinRV statistics,part of the RV and jump the stock price MinRV-J to calculate,then explore the statistical properties of the two,find the RV and MinRV-J and right of rush fat-tailed features.2.Combined with the theory of complex networks,this paper constructs the stock price volatility jump network model by using the minimum spanning tree algorithm(MST),and analyzes the topological properties of network value,centroid and proximity center,It is found that in the whole network,manufacturing,high-tech industry and finance industry are at the core of the position,which is consistent with the key position of these industries in the actual situation of economic development.3.By using the Fast unfolding algorithm to classify the network of stock price volatility jumps that have been constructed,by comparing the average correlation coefficient between stocks in each community,it is found that when the stock price jumps,the manufacturing stock,the financial industry and the manufacturing stock As well as the strong correlation between manufacturing and energy industry stocks. |
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