Volume 55, Number 5, September-October 2021
|Page(s)||2941 - 2961|
|Published online||13 October 2021|
Bi-level optimization approach for robust mean-variance problems
School of Basic Sciences, Indian Institute of Technology Bhubaneswar, Bhubaneswar 752050, Odisha, India
* Corresponding author: firstname.lastname@example.org
Accepted: 14 August 2021
Portfolio Optimization is based on the efficient allocation of several assets, which can get heavily affected by the uncertainty in input parameters. So we must look for such solutions which can give us steady results in uncertain conditions too. Recently, the uncertainty based optimization problems are being dealt with robust optimization approach. With this development, the interest of researchers has been shifted toward the robust portfolio optimization. In this paper, we study the robust counterparts of the uncertain mean-variance problems under box and ellipsoidal uncertainties. We convert those uncertain problems into bi-level optimization models and then derive their robust counterparts. We also solve a problem using this methodology and compared the optimal results of box and ellipsoidal uncertainty models with the nominal model.
Mathematics Subject Classification: 90C17 / 91-08 / 91G10 / 91G15
Key words: Mean-variance model / box uncertainty / ellipsoidal uncertainty / robust optimization / bi-level optimization
© The authors. Published by EDP Sciences, ROADEF, SMAI 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.