论文标题
COVAR具有波动性聚类，重型尾巴和非线性依赖性
CoVaR with volatility clustering, heavy tails and non-linear dependence
论文作者
论文摘要
在本文中，我们通过拟合不同的多元参数模型来估算有条件的价值危险，从而捕获有关多元财务时间股票回报的一些程式化的事实：沉重的尾巴，负偏斜，不对称依赖性和波动性聚类。尽管GJR类型的Ar-Garch动力学获得了挥发性聚类效应，但其他程式化的事实是通过非高斯多元模型和Copula函数捕获的。 COVAR $^{\ leq} $是根据多变量正常模型，多变量恢复稳定稳定（MNT）模型，多变量广义双曲线模型（MGH）和四个可能的copula函数计算的。这些风险度量估计值与基于多元正常GARCH模型的Covar $^{=} $进行了比较。进行比较是通过在2007年1月至2020年3月的时间范围内对竞争对手模型进行回测。在经验研究中，我们考虑了属于主要或全球重要的银行（GSIBS）评估样本的欧元区上市银行的样本。
In this paper we estimate the conditional value-at-risk by fitting different multivariate parametric models capturing some stylized facts about multivariate financial time series of equity returns: heavy tails, negative skew, asymmetric dependence, and volatility clustering. While the volatility clustering effect is got by AR-GARCH dynamics of the GJR type, the other stylized facts are captured through non-Gaussian multivariate models and copula functions. The CoVaR$^{\leq}$ is computed on the basis on the multivariate normal model, the multivariate normal tempered stable (MNTS) model, the multivariate generalized hyperbolic model (MGH) and four possible copula functions. These risk measure estimates are compared to the CoVaR$^{=}$ based on the multivariate normal GARCH model. The comparison is conducted by backtesting the competitor models over the time span from January 2007 to March 2020. In the empirical study we consider a sample of listed banks of the euro area belonging to the main or to the additional global systemically important banks (GSIBs) assessment sample.
