题    目：Sparse Robust Enhanced Indexation Optimization

主讲人：徐凤敏 教授

单    位：西安交通大学

时    间：2026年5月14日 10:00

地    点：郑州校区九章学堂南楼C座209

摘    要： In this paper, we investigate an enhanced indexation methodology using robust Conditional Value-at-Risk (CVaR) and group-sparse optimization. A featured difference from the existing literatures is to describe the tail risk using the worst-case CVaR of excess returns, and the process of industry selection using a weighted $\ell_{\infty,1}$-norm constraint. We develop an accelerated alternating minimization algorithm for solving this problem. At each iteration, this method usually alternately solves a convex cone program, which admits a closed-form solution via convex duality theory, and a projection problem onto a weighted infinity-to-one-ball, where a fixed-point iteration projection method is developed, terminating in finite number of iterations. The global convergence rates in terms of the primal and dual residuals are also provided. Empirical tests on actual data sets are presented to demonstrate the superior out-of-sample performance of our proposed strategy.

简    介：徐凤敏，女，西安交通大学 教授、博士生导师，金融科技系系主任。陕西青年科技奖获得者。中国双选法学会理事，中国双选法学会经济数学与管理数学分会副理事长,中国运筹学学会数学规划分会常务理事。中国运筹学会金融工程与风险管理分会常务理事。SCI 期刊RAIRO-Operational Research 以及SSCI 期刊ITOR 的区域主编。