Portfolio optimization with pw-robustness

This paper investigates a portfolio optimization problem under uncertainty on the stock returns, where the manager seeks to achieve an appropriate trade-off between the expected portfolio return and the risk of loss. The uncertainty set consists of a finite set of scenarios occurring with equal prob...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Virginie Gabrel, Cécile Murat, aurélie Thiele
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2018
Materias:	90C90 Applied mathematics. Quantitative methods T57-57.97 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/aef65b8389be4b87ab45892fec26a22c
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:aef65b8389be4b87ab45892fec26a22c
record_format	dspace
spelling	oai:doaj.org-article:aef65b8389be4b87ab45892fec26a22c2021-12-02T05:01:08ZPortfolio optimization with pw-robustness2192-440610.1007/s13675-018-0096-8https://doaj.org/article/aef65b8389be4b87ab45892fec26a22c2018-09-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2192440621001039https://doaj.org/toc/2192-4406This paper investigates a portfolio optimization problem under uncertainty on the stock returns, where the manager seeks to achieve an appropriate trade-off between the expected portfolio return and the risk of loss. The uncertainty set consists of a finite set of scenarios occurring with equal probability. We introduce a new robustness criterion, called pw-robustness, which seeks to maximize the portfolio return in a proportion p of scenarios and guarantees a minimum return over all scenarios. We model this optimization problem as a mixed-integer programming problem. Through extensive numerical experiments, we identify the instances that can be solved to optimality in an acceptable time using off-the-shelf software. For the instances that cannot be solved to optimality within the time frame, we propose and test a heuristic that exhibits excellent practical performance in terms of computation time and solution quality for the problems we consider. This new criterion and our heuristic methods therefore exhibit great promise to tackle robustness problems when the uncertainty set consists of a large number of scenarios.Virginie GabrelCécile Murataurélie ThieleElsevierarticle90C90Applied mathematics. Quantitative methodsT57-57.97Electronic computers. Computer scienceQA75.5-76.95ENEURO Journal on Computational Optimization, Vol 6, Iss 3, Pp 267-290 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	90C90 Applied mathematics. Quantitative methods T57-57.97 Electronic computers. Computer science QA75.5-76.95
spellingShingle	90C90 Applied mathematics. Quantitative methods T57-57.97 Electronic computers. Computer science QA75.5-76.95 Virginie Gabrel Cécile Murat aurélie Thiele Portfolio optimization with pw-robustness
description	This paper investigates a portfolio optimization problem under uncertainty on the stock returns, where the manager seeks to achieve an appropriate trade-off between the expected portfolio return and the risk of loss. The uncertainty set consists of a finite set of scenarios occurring with equal probability. We introduce a new robustness criterion, called pw-robustness, which seeks to maximize the portfolio return in a proportion p of scenarios and guarantees a minimum return over all scenarios. We model this optimization problem as a mixed-integer programming problem. Through extensive numerical experiments, we identify the instances that can be solved to optimality in an acceptable time using off-the-shelf software. For the instances that cannot be solved to optimality within the time frame, we propose and test a heuristic that exhibits excellent practical performance in terms of computation time and solution quality for the problems we consider. This new criterion and our heuristic methods therefore exhibit great promise to tackle robustness problems when the uncertainty set consists of a large number of scenarios.
format	article
author	Virginie Gabrel Cécile Murat aurélie Thiele
author_facet	Virginie Gabrel Cécile Murat aurélie Thiele
author_sort	Virginie Gabrel
title	Portfolio optimization with pw-robustness
title_short	Portfolio optimization with pw-robustness
title_full	Portfolio optimization with pw-robustness
title_fullStr	Portfolio optimization with pw-robustness
title_full_unstemmed	Portfolio optimization with pw-robustness
title_sort	portfolio optimization with pw-robustness
publisher	Elsevier
publishDate	2018
url	https://doaj.org/article/aef65b8389be4b87ab45892fec26a22c
work_keys_str_mv	AT virginiegabrel portfoliooptimizationwithpwrobustness AT cecilemurat portfoliooptimizationwithpwrobustness AT aureliethiele portfoliooptimizationwithpwrobustness
_version_	1718400848096133120

Portfolio optimization with pw-robustness

Ejemplares similares