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.
Proceedings of the ... IEEE Conference on Decision & Control. IEEE Conference on Decision & Control. 2019 Dec:2019:1526-1531. doi: 10.1109/cdc40024.2019.9029381
Distributionally Robust Portfolio Optimization
分布鲁棒的资产组合优化 翻译改进
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DOI: 10.1109/cdc40024.2019.9029381 PMID: 33723475
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