标题： 具有尾部估计的协方差结构的统计估计，使用节点分位数预测回归模型 显示英文标题

标题： Statistical Estimation for Covariance Structures with Tail Estimates using Nodewise Quantile Predictive Regression Models

摘要： 本文考虑了尾部估计的协方差结构规范。 我们关注两个方面：(i) 在时间序列观测数量多于投资组合中的资产数量的情况下，使用分位数预测回归模型估计VaR-CoVaR风险矩阵，而无需假设存在非平稳回归量；(ii) 构建一种新颖的变量选择算法，称为基于中心性排除的特征排序（FOCE），该算法基于无假设的回归框架，没有调参，并在一般稀疏性假设下被证明是一致的。 我们在建模网络中的系统性风险时，通过真实和模拟数据集的数值研究展示了我们所提出方法的实用性。 显示英文摘要

摘要： This paper considers the specification of covariance structures with tail estimates. We focus on two aspects: (i) the estimation of the VaR-CoVaR risk matrix in the case of larger number of time series observations than assets in a portfolio using quantile predictive regression models without assuming the presence of nonstationary regressors and; (ii) the construction of a novel variable selection algorithm, so-called, Feature Ordering by Centrality Exclusion (FOCE), which is based on an assumption-lean regression framework, has no tuning parameters and is proved to be consistent under general sparsity assumptions. We illustrate the usefulness of our proposed methodology with numerical studies of real and simulated datasets when modelling systemic risk in a network.