论文标题
制作全身风险措施的头或尾巴
Making heads or tails of systemic risk measures
论文作者
论文摘要
This paper shows that the CoVaR,$Δ$-CoVaR,CoES,$Δ$-CoES and MES systemic risk measures can be represented in terms of the univariate risk measure evaluated at a quantile determined by the copula.结果适用于这些措施的经验相关特性，这些措施对它们对幂律尾巴，离群值及其在聚集中的特性的敏感性。此外，提出了一种新的COE经验估计量。幂律结果用于得出幂律系数的新型经验估计量，该系数取决于$δ\ text {-covar}/δ\ text {-coes} $。为了显示经验绩效模拟以及将方法应用于大型金融机构数据集。本文发现，MES不适合测量极端风险。同样，基于ES的措施对幂律尾巴和大损失更加敏感。这使得这些措施对于衡量网络风险而更有用，但对于系统性风险而言较少。鲁棒性分析还表明，由于中间损失的发生，所有$δ$测量都可以低估。最后，发现幂律尾部系数估计器可以用作系统性风险的早期训练指标。
This paper shows that the CoVaR,$Δ$-CoVaR,CoES,$Δ$-CoES and MES systemic risk measures can be represented in terms of the univariate risk measure evaluated at a quantile determined by the copula. The result is applied to derive empirically relevant properties of these measures concerning their sensitivity to power-law tails, outliers and their properties under aggregation. Furthermore, a novel empirical estimator for the CoES is proposed. The power-law result is applied to derive a novel empirical estimator for the power-law coefficient which depends on $Δ\text{-CoVaR}/Δ\text{-CoES}$. To show empirical performance simulations and an application of the methods to a large dataset of financial institutions are used. This paper finds that the MES is not suitable for measuring extreme risks. Also, the ES-based measures are more sensitive to power-law tails and large losses. This makes these measures more useful for measuring network risk but less so for systemic risk. The robustness analysis also shows that all $Δ$ measures can underestimate due to the occurrence of intermediate losses. Lastly, it is found that the power-law tail coefficient estimator can be used as an early-warning indicator of systemic risk.
