摘要
金融关联网络是风险传染的载体。以股票信息溢出关系作为风险关联渠道,并将网络拓扑结构与个体金融机构的风险传染特征紧密结合。基于金融机构股票收益溢出关系,构建信息溢出网络。利用网络节点中心性拓扑结构特征指标,刻画金融机构的风险传染强度及风险承受强度,并挖掘其影响因素。针对中国上市金融机构的实证研究表明:节点度、接近中心性及特征向量中心性较为一致地刻画了金融机构的风险传染特征;信托等其他金融业的平均风险传染强度最大,而银行业金融机构的平均风险承受强度最大;金融机构的每股收益增长率越高、股票换手率越低,风险传染强度越大;金融机构的资产负债率越高、资产规模越大,风险承受强度越大。
The financial network is the channel of risk contagion. In this paper, the information spillover among stocks is regarded as the risk contagion channels among financial institutions, and network topology structures are tightly related with the risk contagion characteristics of the individual financial institution. Besides, the information spillover network is constructed based on the return spillover relationships among financial institutions. In addition, the risk contagion and risk exposure of financial institutions are measured by network centralities and their influence factors are investigated. The empirical study on Chinese listed financial institutions demonstrates that the risk contagion characteristics can be consistently measured by node degree, closeness centrality, and eigenvector centrality. The trust and other financial industry have the largest average risk contagion degree, while the bank industry has the largest average risk exposure degree. The EPS (earnings per share) growth rate has a positive relationship with the risk contagion degree of the financial institution, whereas the stock turnover rate has a negative relationship with it. The leverage and the assets size have positive relationships with the risk exposure degree of the financial institution.
作者
黄玮强
庄新田
姚爽
HUANG Weiqiang1, ZHUANG Xintian 1, YAO Shuang2(1. School of Business Administration, Northeastern University, Shenyang 110819, China; 2. School of Economics and Management, Shenyang University of Chemical Technology, Shenyang 110142, Chin)
出处
《系统管理学报》
CSSCI
CSCD
北大核心
2018年第2期235-243,共9页
Journal of Systems & Management
基金
国家自然科学基金资助项目(71771042
71371044)
关键词
风险传染
信息溢出
金融机构
接近中心性
收益溢出
financial institution
risk contagion
information spillover network
network centrality
influencing factors