Robust Solutions to Least-Squares Problems with Uncertain Data
SIAM Journal on Matrix Analysis and Applications
Mathematics of Operations Research
Robust portfolio selection problems
Mathematics of Operations Research
A class of fuzzy random optimization: expected value models
Information Sciences: an International Journal
Fuzzy random chance-constrained programming
IEEE Transactions on Fuzzy Systems
Fuzzy random dependent-chance programming
IEEE Transactions on Fuzzy Systems
Worst-case VaR and robust portfolio optimization with interval random uncertainty set
Expert Systems with Applications: An International Journal
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This paper addresses the portfolio selection problem in a robust manner. In practice, it is difficult to collect all information to determine the precise bounds of the box uncertainty set used in robust portfolio optimization. To solve this problem, we introduce a novel uncertainty set: interval random uncertainty. We apply our interval random chance-constrained programming to robust semi-absolute deviation portfolio selection under interval random uncertainty in the element of mean vector. The method for generating the uncertainty set from historical data is discussed. An hybrid-intelligent algorithm is applied to solve the robust portfolio model. Finally, we compare the potentially significant economic benefits of investing in portfolios computed using classical model and the model introduced here. And the robustness is achieved at relatively high performance and low cost.