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Numerical solution of a general interval quadratic programming model for portfolio selection

Based on the Markowitz mean variance model, this paper discusses the portfolio selection problem in an uncertain environment. To construct a more realistic and optimized model, in this paper, a new general interval quadratic programming model for portfolio selection is established by introducing the...

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Detalles Bibliográficos
Autores principales: Wang, Jianjian, He, Feng, Shi, Xin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6415890/
https://www.ncbi.nlm.nih.gov/pubmed/30865676
http://dx.doi.org/10.1371/journal.pone.0212913
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author Wang, Jianjian
He, Feng
Shi, Xin
author_facet Wang, Jianjian
He, Feng
Shi, Xin
author_sort Wang, Jianjian
collection PubMed
description Based on the Markowitz mean variance model, this paper discusses the portfolio selection problem in an uncertain environment. To construct a more realistic and optimized model, in this paper, a new general interval quadratic programming model for portfolio selection is established by introducing the linear transaction costs and liquidity of the securities market. Regarding the estimation for the new model, we propose an effective numerical solution method based on the Lagrange theorem and duality theory, which can obtain the effective upper and lower bounds of the objective function of the model. In addition, the proposed method is illustrated with two examples, and the results show that the proposed method is better and more feasible than the commonly used portfolio selection method.
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spelling pubmed-64158902019-04-02 Numerical solution of a general interval quadratic programming model for portfolio selection Wang, Jianjian He, Feng Shi, Xin PLoS One Research Article Based on the Markowitz mean variance model, this paper discusses the portfolio selection problem in an uncertain environment. To construct a more realistic and optimized model, in this paper, a new general interval quadratic programming model for portfolio selection is established by introducing the linear transaction costs and liquidity of the securities market. Regarding the estimation for the new model, we propose an effective numerical solution method based on the Lagrange theorem and duality theory, which can obtain the effective upper and lower bounds of the objective function of the model. In addition, the proposed method is illustrated with two examples, and the results show that the proposed method is better and more feasible than the commonly used portfolio selection method. Public Library of Science 2019-03-13 /pmc/articles/PMC6415890/ /pubmed/30865676 http://dx.doi.org/10.1371/journal.pone.0212913 Text en © 2019 Wang et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Wang, Jianjian
He, Feng
Shi, Xin
Numerical solution of a general interval quadratic programming model for portfolio selection
title Numerical solution of a general interval quadratic programming model for portfolio selection
title_full Numerical solution of a general interval quadratic programming model for portfolio selection
title_fullStr Numerical solution of a general interval quadratic programming model for portfolio selection
title_full_unstemmed Numerical solution of a general interval quadratic programming model for portfolio selection
title_short Numerical solution of a general interval quadratic programming model for portfolio selection
title_sort numerical solution of a general interval quadratic programming model for portfolio selection
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6415890/
https://www.ncbi.nlm.nih.gov/pubmed/30865676
http://dx.doi.org/10.1371/journal.pone.0212913
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