@inproceedings{ddf719b9ae424c8e845a08bb00760982,
title = "Distributionally Robust Portfolio Optimization",
abstract = "In this paper we consider the problem of portfolio optimization involving uncertainty in the probability distribution of the assets returns. Starting with an estimate of the mean and covariance matrix of the returns of the assets, we define a class of admissible distributions for the returns and show that optimizing the worst-case risk of loss can be done in a numerically efficient way. More precisely, we show that determining the asset allocation that minimizes the distributionally robust risk can be done using quadratic programming and a one line search. Effectiveness of the proposed approach is shown using academic examples.",
author = "Bardakci, {I. E.} and Lagoa, {C. M.}",
note = "Funding Information: This work was supported by the National Institutes of Health Grant R01 HL142732 and the National Science Foundation Grant 1808266 I. E. Bardakci and C. Lagoa are with the Department of Electrical Engineering, the Pennsylvania State University, University Park, PA 16802, USA bardakci@psu.edu; lagoa@engr.psu.edu. Publisher Copyright: {\textcopyright} 2019 IEEE.; 58th IEEE Conference on Decision and Control, CDC 2019 ; Conference date: 11-12-2019 Through 13-12-2019",
year = "2019",
month = dec,
doi = "10.1109/CDC40024.2019.9029381",
language = "English (US)",
series = "Proceedings of the IEEE Conference on Decision and Control",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1526--1531",
booktitle = "2019 IEEE 58th Conference on Decision and Control, CDC 2019",
address = "United States",
}