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AI 根据所提供的论文材料归类:摘要明确围绕 CoVaR、金融机构尾部溢出与系统性风险网络展开,直接对应尾部与系统性风险。

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关注这篇论文的作者
Weining Wang
Lining Yu

TENET:尾部事件驱动的网络风险

研究问题
如何在高维金融机构系统中联合描述尾部事件与网络动态,估计非线性的风险溢出,并识别系统重要性机构?
方法与思路
作者提出半参数系统关联度量,在广义分位数回归的单指标模型中进行时间序列变量选择,再分析尾部风险网络。
材料陈述的贡献
摘要称 TENET 可识别风险接收与输出机构;其美国金融市场应用报告了不同金融行业在危机时期的风险关联差异。
阅读关注点
应核对尾部关联度量、变量选择与单指标估计步骤,以及网络边的方向、时间变化和机构重要性的定义。
阅读边界
仅据所给出版社摘要,无法判断尾部估计误差和网络结论的稳健性;所述溢出关联也不足以直接确立因果传染。

原文摘要

CoVaR is a measure for systemic risk of the networked financial system conditional on institutions being under distress. The analysis of systemic risk is the focus of recent econometric analyses and uses tail event and network based techniques. Here, in this paper we bring tail event and network dynamics together into one context. In order to pursue such joint efforts, we propose a semiparametric measure to estimate systemic interconnectedness across financial institutions based on tail-driven spillover effects in a high dimensional framework. The systemically important institutions are identified conditional to their interconnectedness structure. Methodologically, a variable selection technique in a time series setting is applied in the context of a single-index model for a generalized quantile regression framework. We could thus include more financial institutions into the analysis to measure their tail event interdependencies and, at the same time, be sensitive to non-linear relationships between them. Network analysis, its behaviour and dynamics, allows us to characterize the role of each financial industry group in 2007–2012: the depositories received and transmitted more risk among other groups, the insurers were less affected by the financial crisis. The proposed TENET - Tail Event driven NETwork technique allows us to rank the Systemic Risk Receivers and Systemic Risk Emitters in the US financial market.

笔记与分组
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