Research on the Invulnerability and Early Warning of Global Stock Market Networks Under the Background of Epidemic
ZHAO Junchan1, WANG Shaowei1, LU Jun'an2, WANG Jingtong1
1. a. School of Science; b. Human Key Laboratory of Human Province for Statistics Learning and Intelligent Computation, Hunan University of Business and Technology, Changsha 410205, China; 2. The School of Mathematics and Statistics, Wuhan University, Wuhan 430072, China
Abstract：The Covid-19 epidemic has a significant impact on the global economy. In this paper, the minimum spanning tree method and threshold method are combined to build a correlation network for three periods before the outbreak, domestic outbreak and global spread of the epidemic. By comparing the network topology, survivability and node importance in the three stages, it is found that the epidemic has significantly enhanced the linkage effect between global stock markets. The global stock correlation network has obvious small-world characteristics, and the betweenness of nodes obey power law distribution. In the simulation experiment of network attack, deliberate attack is more destructive than random attack, and the robustness of global stock network in three periods is enhanced successively. The importance of stocks changed significantly before and after the epidemic. Chinese mainland and Hong Kong were the first to suffer a huge impact in the epidemic, but they quickly adjusted, while Europe and The United States were only affected after the global epidemic worsened. In addition, the application of cumulative sum control chart (CUSUM) to the early warning of stock prices has also achieved good results.
赵军产, 王少薇, 陆君安, 王敬童. 疫情背景下全球股市网络的抗毁性及预警研究[J]. 复杂系统与复杂性科学, 2022, 19(1): 52-59.
ZHAO Junchan, WANG Shaowei, LU Jun'an, WANG Jingtong. Research on the Invulnerability and Early Warning of Global Stock Market Networks Under the Background of Epidemic. Complex Systems and Complexity Science, 2022, 19(1): 52-59.
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