管理评论

管理评论 ›› 2021, Vol. 33 ›› Issue (4): 59-70.

• 经济与金融管理 • 上一篇    下一篇

金融系统的网络结构及尾部风险度量——基于动态半参数分位数回归模型

张兴敏1, 傅强2, 张帅2, 季俊伟3   

  1. 1. 西南财经大学金融学院, 成都 611130;
    2. 重庆大学经济与工商管理学院, 重庆 400044;
    3. 成都理工大学商学院, 成都 610059
  • 收稿日期:2018-02-23 出版日期:2021-04-28 发布日期:2021-05-06
  • 通讯作者: 张兴敏（通讯作者），西南财经大学金融学院讲师，博士
  • 作者简介:傅强，重庆大学经济与工商管理学院教授，博士生导师，博士；张帅，重庆大学经济与工商管理学院博士研究生；季俊伟，成都理工大学商学院讲师，博士。
  • 基金资助:
    重庆市研究生科研创新项目（CYB19025）。

Financial System Network Contagion Structure and Tail Risk Measurement Based on Dynamic Semiparametric Quantile Regression Model

Zhang Xingmin1, Fu Qiang2, Zhang Shuai2, Ji Junwei3   

  1. 1. School of Finance, Southwestern University of Finance and Economics, Chengdu 611130;
    2. School of Economics and Business Administration, Chongqing University, Chongqing 400044;
    3. Business School, Chengdu University of Technology, Chengdu 610059
  • Received:2018-02-23 Online:2021-04-28 Published:2021-05-06

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摘要： 运用动态半参数分位数回归模型构建金融网络结构，厘清金融机构间的极端风险传染效应，并将市场情绪指标作为条件变量引入网络模型中。研究发现，金融机构的系统性风险贡献和暴露以及尾部风险传染度的统计特征（如排名）存在显著差异。金融机构的网络传染度排名，尾部风险接收源排名和尾部风险发射源排名存在显著差异。在经济金融动荡时期，三大金融行业（银行业、证券业和保险业）的内部网络传染效应均显著增强，且证券业内的传染效应明显高于银行业内的传染效应。基于滚动窗宽选择标准的金融网络模型优化了金融机构间的风险传染关系的时变性识别过程。对高传染性的金融机构实施监管已成为一个至关重要的政策问题，因此探究极端风险的网络关联性有助于提升对金融系统的监督和监管效率，本文的研究框架为此提供了相应依据。

关键词: 系统性风险, CoVaR, 半参数回归, 网络传染关系, 市场情绪

Abstract: This paper applies the dynamic semiparametric quantile regression model to construct network structure, clarifying the extreme risk contagion effects among financial institutions. We consider the market sentiments as conditional variables in the network model. The results show that the systemic risk contribution and exposure as well as tail risk contagion degree hold remarkable different information and characteristics. The network contagion degree ranking of financial firms, the tail risk receiver ranking, and tail risk emitter ranking are significant heterogeneous. During the economic and financial turmoil, the network contagion effects within three financial sectors (banking, securities, and insurances) have risen sharply, and the interconnectedness within securities is significantly higher and more volatile than between banks. The financial network model based on the rolling window width selection criteria optimizes the time-varying identification process of risk contagion relationships among financial institutions. The supervision on the highly contagious financial institutions has become a crucial policy issue. Therefore, exploring the network dependence of extreme risks is crucial for financial regulators to improve the supervision efficiency of the financial system.

Key words: systemic risk, CoVaR, semiparametric regression, network contagion, market sentiment