伊利诺伊大学芝加哥分校周文心副教授：预期短缺回归

报告摘要
Expected Shortfall (ES), also known as superquantile or Conditional Value-at-Risk, has been recognized as an important measure in risk analysis and stochastic optimization. In finance, it refers to the conditional expected return of an asset given that the return is below some quantile of its distribution. In this talk, we consider a joint regression framework that simultaneously models the conditional quantile and ES of a response variable given a set of covariates, for which the state-of-the-art approach is based on minimizing a joint loss function that is non-differentiable and non-convex. 

Motivated by the idea of using orthogonal scores to reduce sensitivity with respect to nuisance parameters, we study a unified two-step framework for fitting joint quantile and ES regression models under three settings: (i) linear models, (ii) sparse linear models in high dimensions, and (iii) nonparametric models in RKHS. We establish finite-sample properties for the proposed estimators along with their robust counterparts and propose different inference methods under various model structures. A new Python package, named quantes (https://pypi.org/project/quantes/), is developed to implement ES regressions.

嘉宾简介
Wenxin Zhou is an associate professor in the Department of Information and Decision Sciences, College of Business Administration at the University of Illinois Chicago. Prior to joining UIC, he was a faculty member in the Department of Mathematics at the University of California, San Diego. His research interests include high-dimensional statistical inference, robust statistics, quantile regression, nonparametric statistics, and deep learning.

直播分享时间：2024年9月7日