Buổi 4: Thứ 5 ngày 02/06/2022 (20h00- 21h00)

Talk 5: Mean-Covariance Robust Risk Measurement

Abstract:

We introduce a universal framework for mean-covariance robust risk measurement and portfolio optimization. We model uncertainty in terms of the Gelbrich distance on the mean-covariance space, along with prior structural information about the population distribution. Our approach is related to the theory of optimal transport and exhibits superior statistical and computational properties than existing models. We find that, for a large class of risk measures, mean-covariance robust portfolio optimization boils down to the Markowitz model, subject to a regularization term given in closed form. This includes the finance standards, value-at-risk and conditional value-at-risk, and can be solved highly efficiently. This is a joint work with Soroosh Shafieezadeh-Abadeh, Damir Filipovic and Daniel Kuhn.

Bio:

Dr. Viet Anh Nguyen is a research scientist at VinAI Research, Vietnam. Previously, he was a postdoctoral scholar at the Department of Management Science and Engineering, Stanford University. He holds a B.Eng and a M.Eng from the National University of Singapore, a French engineering diploma (Diplome d’Ingenieur) from Ecole Centrale Paris, and a Ph.D. from Ecole Polytechnique Federale de Lausanne. He is interested in very large-scale decision making under uncertainty, statistical optimization and machine learning with applications in autonomous systems, operations management, and data/policy analytics