Tail Risk in the Tail: Estimating High Quantiles When a Related Variable is Extreme
研究当相关变量处于极端状态时，如何估计焦点变量的高分位数，用于评估金融市场的系统性风险（如条件风险价值CoVaR），提出基于调整因子的新估计方法并验证其效果。
In this article we address the problem of high quantile estimation conditional on a related variable being extreme. The problem set-up is of interest in a number applications to evaluate tail risk of a focal variable in the tail of a conditioning variable. A primary example we consider is the assessment of systemic risk in financial markets using a risk measure known as the conditional value-at-risk (CoVaR). The proposed estimator is based on a novel approach to handle the bivariate tail dependence structure through an adjustment factor that can be used in conjunction with univariate high quantile estimation techniques. We establish the asymptotic behavior of the estimator under relatively weak assumptions, and illustrate its performance via simulation studies and a real data example. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.