In this paper, a sensitivity analysis using pair–copula decomposition of multivariate dependency models is performed on estimates of value-at-risk (VaR) and conditional value-at-risk (CVaR). To illustrate the results, we use four financial share portfolios selected to exemplify this purpose. For each share, we calculate filtered log returns using autoregressive moving average–generalized autoregressive conditional heteroscedasticity models and study their dependence. We analyze how selecting pairs of assets to define vines prior to pair–copula decomposition affects the estimated VaR and CVaR. Further, using bootstrap confidence intervals, we compare the results of different risk measures obtained by employing alternative measures of dependence to select the order in which the drawable vine (D-vine) is defined in different portfolios. Moreover, we carry out a simulation study to analyze the finite sample properties of the different criteria for selecting the pair–copula decomposition associated with the D-vine. We find some differences between the results obtained for VaR and CVaR.

中文翻译：

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D-vine结构对风险估计的影响

在本文中，对风险价值 (VaR) 和条件风险价值 (CVaR) 的估计使用多变量依赖模型的对-copula 分解进行了敏感性分析。为了说明结果，我们使用选定的四个金融股票投资组合来举例说明这一目的。对于每个份额，我们使用自回归移动平均 - 广义自回归条件异方差模型计算过滤对数回报并研究它们的依赖性。我们分析了在pair-copula分解之前选择资产对来定义葡萄藤如何影响估计的VaR和CVaR。此外，使用自举置信区间，我们比较了通过采用替代依赖度量来选择可绘制藤蔓 (D-vine) 在不同投资组合中定义的顺序而获得的不同风险度量的结果。而且，我们进行了一项模拟研究，以分析选择与 D-vine 相关联的对 - copula 分解的不同标准的有限样本属性。我们发现 VaR 和 CVaR 获得的结果之间存在一些差异。