英文标题：
《Interconnected risk contributions: an heavy-tail approach to analyse US
  financial sectors》
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作者：
M. Bernardi, L. Petrella
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最新提交年份：
2014
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英文摘要：
  In this paper we consider a multivariate model-based approach to measure the dynamic evolution of tail risk interdependence among US banks, financial services and insurance sectors. To deeply investigate the risk contribution of insurers we consider separately life and non-life companies. To achieve this goal we apply the multivariate student-t Markov Switching model and the Multiple-CoVaR (CoES) risk measures introduced in Bernardi et. al. (2013b) to account for both the known stylised characteristics of the data and the contemporaneous joint distress events affecting financial sectors. Our empirical investigation finds that banks appear to be the major source of risk for all the remaining sectors, followed by the financial services and the insurance sectors, showing that insurance sector significantly contributes as well to the overall risk. Moreover, we find that the role of each sector in contributing to other sectors distress evolves over time accordingly to the current predominant financial condition, implying different interconnection strength.
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中文摘要：
在本文中，我们考虑一种基于多变量模型的方法来衡量美国银行、金融服务和保险业之间尾部风险相互依赖的动态演变。为了深入调查保险公司的风险贡献，我们分别考虑人寿保险公司和非人寿保险公司。为了实现这一目标，我们应用多元student-t马尔可夫转换模型和Bernardi等人（2013b）提出的多重CoVaR（CoES）风险度量来解释数据的已知风格化特征和影响金融部门的同期联合危机事件。我们的实证调查发现，银行似乎是其余所有行业的主要风险来源，其次是金融服务业和保险业，表明保险业对整体风险也有显著贡献。此外，我们发现，每个部门在导致其他部门陷入困境方面的作用会随着时间的推移而相应地演变，这意味着不同的互联强度。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Risk Management        风险管理
分类描述：Measurement and management of financial risks in trading, banking, insurance, corporate and other applications
衡量和管理贸易、银行、保险、企业和其他应用中的金融风险
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