摘要翻译：
最近的金融灾难强调了调查机构之间尾部合作的后果的必要性；传染的事件经常被观察到，并增加了影响市场参与者风险资本的巨大损失的可能性。通常使用的风险管理工具未能考虑到机构之间的潜在溢出效应，因为它们提供了个人风险评估。我们致力于分析极端事件的相互依赖效应，为评估条件风险价值(CoVaR)提供一个估计工具，条件风险价值定义为一个机构在另一个机构处于困境的条件下的风险价值。特别地，我们的方法依赖于贝叶斯分位数回归框架。我们提出了一种马尔可夫链蒙特卡罗算法，该算法利用了非对称拉普拉斯分布，并将其表示为法线的位置-尺度混合。此外，由于风险度量通常是在时间序列数据上评估的，回报通常随时间变化，我们扩展了CoVaR模型来考虑尾部行为的动力学。应用标准普尔综合指数(S&P500)对美国不同行业的公司进行评估，以评估各机构对整体系统风险的边际贡献
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英文标题：
《Bayesian inference for CoVaR》
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作者：
Mauro Bernardi, Ghislaine Gayraud, Lea Petrella
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最新提交年份：
2013
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分类信息：

一级分类：Statistics 统计学
二级分类：Methodology 方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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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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一级分类：Quantitative Finance 数量金融学
二级分类：Statistical Finance 统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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英文摘要：
Recent financial disasters emphasised the need to investigate the consequence associated with the tail co-movements among institutions; episodes of contagion are frequently observed and increase the probability of large losses affecting market participants\' risk capital. Commonly used risk management tools fail to account for potential spillover effects among institutions because they provide individual risk assessment. We contribute to analyse the interdependence effects of extreme events providing an estimation tool for evaluating the Conditional Value-at-Risk (CoVaR) defined as the Value-at-Risk of an institution conditioned on another institution being under distress. In particular, our approach relies on Bayesian quantile regression framework. We propose a Markov chain Monte Carlo algorithm exploiting the Asymmetric Laplace distribution and its representation as a location-scale mixture of Normals. Moreover, since risk measures are usually evaluated on time series data and returns typically change over time, we extend the CoVaR model to account for the dynamics of the tail behaviour. Application on U.S. companies belonging to different sectors of the Standard and Poor\'s Composite Index (S&P500) is considered to evaluate the marginal contribution to the overall systemic risk of each individual institution
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