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.
|Title of host publication
|2019 IEEE 58th Conference on Decision and Control, CDC 2019
|Institute of Electrical and Electronics Engineers Inc.
|Number of pages
|Published - Dec 2019
|58th IEEE Conference on Decision and Control, CDC 2019 - Nice, France
Duration: Dec 11 2019 → Dec 13 2019
|Proceedings of the IEEE Conference on Decision and Control
|58th IEEE Conference on Decision and Control, CDC 2019
|12/11/19 → 12/13/19
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Modeling and Simulation
- Control and Optimization