系统性风险
构造(python库)
订单(交换)
金融危机
排名(信息检索)
业务
财务
秩(图论)
经济
金融体系
计算机科学
数学
组合数学
机器学习
宏观经济学
程序设计语言
作者
Sihua Tian,Shaofang Li,Qinen Gu
标识
DOI:10.1080/13504851.2023.2298420
摘要
Identifying systemically important financial institutions (SIFIs) plays a key role in regulating systemic risk. We construct higher-order temporal causal networks based on daily stock return data and rank the systemic importance of 45 listed financial institutions. The constructed networks provide evidence that financial institutions become more interrelated during crisis periods, and capture the higher-order dynamic characteristics of interconnectedness among institutions. The ranking results show that not only the largest financial institutions, but also smaller, highly interconnected institutions are systemically important and need to be regulated. Our findings highlight the importance of considering higher-order interconnectedness among financial institutions when labelling SIFIs.
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