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2014 Distributionally Robust Return-Risk Optimization Models and Their Applications
Li Yang, Yanxi Li, Zhengyong Zhou, Kejing Chen
J. Appl. Math. 2014: 1-9 (2014). DOI: 10.1155/2014/784715

Abstract

Based on the risk control of conditional value-at-risk, distributionally robust return-risk optimization models with box constraints of random vector are proposed. They describe uncertainty in both the distribution form and moments (mean and covariance matrix of random vector). It is difficult to solve them directly. Using the conic duality theory and the minimax theorem, the models are reformulated as semidefinite programming problems, which can be solved by interior point algorithms in polynomial time. An important theoretical basis is therefore provided for applications of the models. Moreover, an application of the models to a practical example of portfolio selection is considered, and the example is evaluated using a historical data set of four stocks. Numerical results show that proposed methods are robust and the investment strategy is safe.

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Li Yang. Yanxi Li. Zhengyong Zhou. Kejing Chen. "Distributionally Robust Return-Risk Optimization Models and Their Applications." J. Appl. Math. 2014 1 - 9, 2014. https://doi.org/10.1155/2014/784715

Information

Published: 2014
First available in Project Euclid: 2 March 2015

zbMATH: 07131858
Digital Object Identifier: 10.1155/2014/784715

Rights: Copyright © 2014 Hindawi

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