A combinatorial optimization approach to scenario filtering in portfolio selection

Recent studies stressed the fact that covariance matrices computed from empirical financial time series appear to contain a high amount of noise. This makes the classical Markowitz Mean–Variance Optimization model unable to correctly evaluate the performance associated to selected portfolios. Since...

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Autores: Puerto Albandoz, Justo, Ricca, Federica, Rodríguez Madrena, Moisés, Scozzari, Andrea
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2022
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/131000
Acceso en línea:https://hdl.handle.net/11441/131000
https://doi.org/10.1016/j.cor.2022.105701
Access Level:acceso abierto
Palabra clave:Mean–Variance optimization
Portfolio selection
Filtering methods
Mixed integer quadratic programming
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spelling A combinatorial optimization approach to scenario filtering in portfolio selectionPuerto Albandoz, JustoRicca, FedericaRodríguez Madrena, MoisésScozzari, AndreaMean–Variance optimizationPortfolio selectionFiltering methodsMixed integer quadratic programmingRecent studies stressed the fact that covariance matrices computed from empirical financial time series appear to contain a high amount of noise. This makes the classical Markowitz Mean–Variance Optimization model unable to correctly evaluate the performance associated to selected portfolios. Since the Markowitz model is still one of the most used practitioner-oriented tool, several filtering methods have been proposed in the literature to overcome the problem. Among them, the two most promising ones refer to the Random Matrix Theory and to the Power Mapping strategy. The basic idea of these methods is to transform the estimated correlation matrix before applying the Mean–Variance Optimization model. However, experimental analysis shows that these two strategies are not always effective when applied to real financial datasets. In this paper we propose a new filtering method based on Quadratic Programming. We develop a Mixed Integer Quadratic Programming model, which is able to filter those observations that may affect the performance of the selected portfolio. We discuss the properties of this new model and test it on some real financial datasets. We compare the out-of-sample performance of our portfolios with the one of the portfolios provided by the two above mentioned alternative filtering methods giving evidence that our method outperforms them. Although our model can be solved efficiently with standard optimization solvers, the computational burden increases for large datasets. To solve also these problems, we propose a heuristic procedure, which, on the basis of our empirical results, shows to be both efficient and effective.ElsevierEstadística e Investigación OperativaFQM331: Metodos y Modelos de la Estadistica y la Investigacion Operativa2022info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/131000https://doi.org/10.1016/j.cor.2022.105701reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésComputers & Operations Research Article Publishing Charge, 142 (June), 105701-1-105701-14.http://dx.doi.org/10.1016/j.cor.2022.105701info:eu-repo/semantics/openAccessoai:idus.us.es:11441/1310002026-06-17T12:51:07Z
dc.title.none.fl_str_mv A combinatorial optimization approach to scenario filtering in portfolio selection
title A combinatorial optimization approach to scenario filtering in portfolio selection
spellingShingle A combinatorial optimization approach to scenario filtering in portfolio selection
Puerto Albandoz, Justo
Mean–Variance optimization
Portfolio selection
Filtering methods
Mixed integer quadratic programming
title_short A combinatorial optimization approach to scenario filtering in portfolio selection
title_full A combinatorial optimization approach to scenario filtering in portfolio selection
title_fullStr A combinatorial optimization approach to scenario filtering in portfolio selection
title_full_unstemmed A combinatorial optimization approach to scenario filtering in portfolio selection
title_sort A combinatorial optimization approach to scenario filtering in portfolio selection
dc.creator.none.fl_str_mv Puerto Albandoz, Justo
Ricca, Federica
Rodríguez Madrena, Moisés
Scozzari, Andrea
author Puerto Albandoz, Justo
author_facet Puerto Albandoz, Justo
Ricca, Federica
Rodríguez Madrena, Moisés
Scozzari, Andrea
author_role author
author2 Ricca, Federica
Rodríguez Madrena, Moisés
Scozzari, Andrea
author2_role author
author
author
dc.contributor.none.fl_str_mv Estadística e Investigación Operativa
FQM331: Metodos y Modelos de la Estadistica y la Investigacion Operativa
dc.subject.none.fl_str_mv Mean–Variance optimization
Portfolio selection
Filtering methods
Mixed integer quadratic programming
topic Mean–Variance optimization
Portfolio selection
Filtering methods
Mixed integer quadratic programming
description Recent studies stressed the fact that covariance matrices computed from empirical financial time series appear to contain a high amount of noise. This makes the classical Markowitz Mean–Variance Optimization model unable to correctly evaluate the performance associated to selected portfolios. Since the Markowitz model is still one of the most used practitioner-oriented tool, several filtering methods have been proposed in the literature to overcome the problem. Among them, the two most promising ones refer to the Random Matrix Theory and to the Power Mapping strategy. The basic idea of these methods is to transform the estimated correlation matrix before applying the Mean–Variance Optimization model. However, experimental analysis shows that these two strategies are not always effective when applied to real financial datasets. In this paper we propose a new filtering method based on Quadratic Programming. We develop a Mixed Integer Quadratic Programming model, which is able to filter those observations that may affect the performance of the selected portfolio. We discuss the properties of this new model and test it on some real financial datasets. We compare the out-of-sample performance of our portfolios with the one of the portfolios provided by the two above mentioned alternative filtering methods giving evidence that our method outperforms them. Although our model can be solved efficiently with standard optimization solvers, the computational burden increases for large datasets. To solve also these problems, we propose a heuristic procedure, which, on the basis of our empirical results, shows to be both efficient and effective.
publishDate 2022
dc.date.none.fl_str_mv 2022
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/131000
https://doi.org/10.1016/j.cor.2022.105701
url https://hdl.handle.net/11441/131000
https://doi.org/10.1016/j.cor.2022.105701
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Computers & Operations Research Article Publishing Charge, 142 (June), 105701-1-105701-14.
http://dx.doi.org/10.1016/j.cor.2022.105701
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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