D-vine copula based quantile regression
D-vine copula based quantile regression
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DOI：
10.1016/j.csda.2016.12.009
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发表时间：
2017-06-01
影响因子：
1.8
通讯作者：
Czado, Claudia
中科院分区：
文献类型：
--
作者：
Kraus, Daniel;Czado, Claudia
分位数回归是对条件分位数的预测，在统计建模和财务应用中稳步获得了重要性。引入了一种新的半参数回归方法。它基于依次拟合了可能的最佳D-vine Copula与给定数据，从而产生了具有易于提取的条件分位数的高度灵活模型。 D-Vines作为常规葡萄藤的子类，以双变量构建块（一种所谓的成对孔子结构（PCC））对多变量Copulas进行建模。所提出的算法即使在高维度中也可以快速准确地工作，并通过最大化条件对数类似物的方式结合了自动变量选择。此外，典型的分位数回归问题（例如分位数交叉或变换，变量的相互作用和共线性）会自动照顾。在模拟研究中，与已建立的分位数回归方法相比，方法的提高了准确性和减少的计算时间。国际信用违约掉期（CDS）数据（包括压力测试和价值风险（VAR）预测）的广泛财务应用证明了该方法的有用性。 （c）2016 Elsevier B.V.保留所有权利。
Quantile regression, that is the prediction of conditional quantiles, has steadily gained importance in statistical modeling and financial applications. A new semiparametric quantile regression method is introduced. It is based on sequentially fitting a likelihood optimal D-vine copula to given data resulting in highly flexible models with easily extractable conditional quantiles. As a subclass of regular vine copulas, D-vines enable the modeling of multivariate copulas in terms of bivariate building blocks, a so-called pair-copula construction (PCC). The proposed algorithm works fast and accurate even in high dimensions and incorporates an automatic variable selection by maximizing the conditional log-likelihood. Further, typical issues of quantile regression such as quantile crossing or transformations, interactions and collinearity of variables are automatically taken care of. In a simulation study the improved accuracy and reduced computation time of the approach in comparison with established quantile regression methods is highlighted. An extensive financial application to international credit default swap (CDS) data including stress testing and Value-at-Risk (VaR) prediction demonstrates the usefulness of the proposed method. (C) 2016 Elsevier B.V. All rights reserved.