【报告摘要】Suppose X is some interesting loss and Y is a benchmark variable. Given some extreme scenarios of Y, it is indispensable to measure the tail risk of X by applying a class of univariate risk measures to study the co-movement of the two variables. In this talk, we consider the extreme and nonparametric inference for the distortion risk measures on the tail regions when the extreme scenarios of some benchmark variable are considered. We derive the limit of the proposed risk measures based on Extreme Value Theory. The asymptotics of the risk measures shows the decomposition of the marginal extreme index and the extreme dependence structure which implies how these two pieces of information have influences on the limit of the risk measures. Finally, for practical purpose, we develop a nonparametric estimation method for the distortion risk measures on tail regions and its asymptotic normality is derived.
【报告人简介】王星，中国科学院数学与系统科学研究院副研究员。主要研究方向为风险量化，风险管理， 极值理论，统计推断与数据科学。相关学术成果发表在Journal of the American Statistical Association (JASA)，Insurance: Mathematics and Economics (IME), European Actuarial Journal, IEEE汇刊等。曾荣获北美精算协会James Hickman学者奖、北美财险协会（CAS）个人科研竞赛奖、美国统计学会女性统计与数据科学家早期职业奖、数据科学与统计论坛早期职业(Early Career)旅行奖，美国质量学会FTC早期职业基金奖等。研究成果两次获得美国运筹与管理学会(INFORMS)最佳理论论文奖提名奖。以独立PI身份主持多项科研项目(总经费约230万)。获北美精算协会SOA的Associate（准精算师）资质以及美国精算学会会员（MAAA）资质认证。目前担任IEEE TNNLS 杂志副主编。
