为研究新冠肺炎疫情对全球经济造成的影响,采用最小生成树法和阈值法相结合的方式构建了疫情爆发前、中国国内疫情爆发和全球疫情蔓延3个时期的全球重要股指的关联网络。通过比较3个阶段的网络拓扑结构、抗毁性和节点重要性,发现疫情使得全球股市之间的联动效应显著增强;全球股票关联网络具有明显的小世界特性,节点的介数服从幂律分布;网络攻击仿真实验中,蓄意攻击比随机攻击更具破坏性,且3个时期的全球股票网络的稳健性依次增强;疫情前后股票的重要性排序发生了明显的变化,中国内地和中国香港在此次疫情中率先遭受了巨大的冲击,但随后很快调整过来,欧美地区则在全球疫情加重后才受到波及。此外,将累积和控制图应用于股价的预警也得到了很好的效果。
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.
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