Abstract

CoVaR (conditional value-at-risk) is a crucial measure for assessing financial systemic risk, which is defined as a conditional quantile of a random variable, conditioned on other random variables reaching specific quantiles. It enables the measurement of risk associated with a particular node in financial networks, taking into account the simultaneous influence of risks from multiple correlated nodes. However, estimating CoVaR presents challenges due to the unobservability of the multivariate-quantiles condition. To address the challenges, we propose a two-step nonparametric estimation approach based on Monte-Carlo simulation data. In the first step, we estimate the unobservable multivariate-quantiles using order statistics. In the second step, we employ a kernel method to estimate the conditional quantile conditional on the order statistics. We establish the consistency and asymptotic normality of the two-step estimator, along with a bandwidth selection method. The results demonstrate that, under a mild restriction on the bandwidth, the estimation error arising from the first step can be ignored. Consequently, the asymptotic results depend solely on the estimation error of the second step, as if the multivariate-quantiles in the condition were observable. Numerical experiments demonstrate the favorable performance of the two-step estimator.

Speaker Intro

南京大学工程管理学院金融科技与工程系助理研究员。研究领域主要为金融数学与金融工程、运筹学与管理科学、概率论与数理统计等。黄伟桓于2020年在山东大学取得数学博士学位，2020-2022年在复旦大学做博士后研究工作。黄伟桓的研究成果获得过中国运筹学会金融工程与金融风险管理分会青年学者最佳论文一等奖等。黄伟桓主持国家自然科学基金一项，参与国自科重大项目等多项，现任中国运筹学会金融工程与金融风险管理分会青年理事，为国内外多个著名期刊的匿名审稿人。