文献类型：期刊文献

英文题名：Tail dependence networks of global stock markets

作者：Wen, Fenghua[1,2];Yang, Xin[3];Zhou, Wei-Xing[4,5]

机构：[1]Cent South Univ, Sch Business, Changsha, Hunan, Peoples R China;[2]Univ Windsor, Supply Chain Management & Logist Optimizat Res Ct, Fac Engn, Windsor, ON, Canada;[3]Changsha Univ Sci & Technol, Sch Math & Stat, Changsha, Hunan, Peoples R China;[4]East China Univ Sci & Technol, Dept Finance, Shanghai, Peoples R China;[5]East China Univ Sci & Technol, Dept Math, Shanghai, Peoples R China

年份：2019

卷号：24

期号：1

起止页码：558

外文期刊名：INTERNATIONAL JOURNAL OF FINANCE & ECONOMICS

收录：;WOS:【SSCI(收录号:WOS:000455484400035)】；

基金：Fundamental Research Funds for the Central Universities, Grant/Award Number: 222201718006; National Natural Science Foundation of China, Grant/Award Number: 71873146, 71873147, 71532009 and 71431008

语种：英文

外文关键词：community structure; complex network; Pearson's correlation coefficient; SJC copula; stock market

摘要：The Pearson correlation coefficient is used by many researchers to construct complex financial networks. However, it is difficult to capture the structural characteristics of financial markets that have extreme fluctuations. To solve this problem, we resort to tail dependence networks. We first build the edge information of the stock network by adopting Pearson's correlation coefficient and the symmetrized Joe-Clayton copula model, respectively. By using the planar maximally filtered graph method, we filter the edge information, obtain Pearson's correlation coefficient and tail dependence network, and compare their efficiencies. The community structure of the constructed networks is investigated. We find that the global efficiency of tail-dependent networks is higher than that of the Pearson correlation networks. Further analysis of the nodes in the upper- and lower-tail dependence networks reveals that the European markets are more influential than Asian and African markets during a booming market and a recession market. In addition, different cliques are found in the two tail dependence networks. The finding indicates that financial risks will impact geographically adjacent markets.