Risk mitigation through noise reduction in hierarchical portfolio selection

Risk parity portfolio methods rely solely on covariance estimates to minimize risk, ignoring expected returns due to their high estimation error. This approach can be unstable when dealing with a reduced number of observations. We address this limitation by improving the signal-to-noise ratio in cov...

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Detalhes bibliográficos
Autores: Salas-Molina, Francisco, Nin, Jordi
Formato: artículo
Fecha de publicación:2026
País:España
Recursos:Universitat Ramon Llull (URL)
Repositorio:DAU Arxiu Digital de la Universitat Ramon Llull
OAI Identifier:oai:dau.url.edu:20.500.14342/6020
Acesso em linha:http://hdl.handle.net/20.500.14342/6020
https://doi.org/10.1016/j.eswa.2025.130304
Access Level:acceso abierto
Palavra-chave:Hierarchical portfolio selection
Backbone extraction
Shrinkage covariance
Risk parity
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spelling Risk mitigation through noise reduction in hierarchical portfolio selectionSalas-Molina, FranciscoNin, JordiHierarchical portfolio selectionBackbone extractionShrinkage covarianceRisk parityRisk parity portfolio methods rely solely on covariance estimates to minimize risk, ignoring expected returns due to their high estimation error. This approach can be unstable when dealing with a reduced number of observations. We address this limitation by improving the signal-to-noise ratio in covariance and correlation matrix estimation within hierarchical portfolio selection models. Our approach combines shrinkage covariance estimation, a backbone network extraction, and density-based clustering method. We test two workflows: one for covariance and one for correlation matrices across four real-world market datasets (S&P, Dow Jones, Euro Stoxx 50, Ibex 35) and a synthetic dataset. Results show improved out-of-sample performance in terms of value-at-risk and conditional value-at-risk, offering a more robust alternative to standard hierarchical risk parity.info:eu-repo/semantics/publishedVersionElsevier Ltd.Universitat Ramon Llull. Esade202620262026info:eu-repo/semantics/article15 p.application/pdfhttp://hdl.handle.net/20.500.14342/6020https://doi.org/10.1016/j.eswa.2025.130304reponame:DAU Arxiu Digital de la Universitat Ramon Llullinstname:Universitat Ramon Llull (URL)InglésExpert Systems with Applications, Vol. 299, Part D, 130304© L'autor/aAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:dau.url.edu:20.500.14342/60202026-06-21T06:40:37Z
dc.title.none.fl_str_mv Risk mitigation through noise reduction in hierarchical portfolio selection
title Risk mitigation through noise reduction in hierarchical portfolio selection
spellingShingle Risk mitigation through noise reduction in hierarchical portfolio selection
Salas-Molina, Francisco
Hierarchical portfolio selection
Backbone extraction
Shrinkage covariance
Risk parity
title_short Risk mitigation through noise reduction in hierarchical portfolio selection
title_full Risk mitigation through noise reduction in hierarchical portfolio selection
title_fullStr Risk mitigation through noise reduction in hierarchical portfolio selection
title_full_unstemmed Risk mitigation through noise reduction in hierarchical portfolio selection
title_sort Risk mitigation through noise reduction in hierarchical portfolio selection
dc.creator.none.fl_str_mv Salas-Molina, Francisco
Nin, Jordi
author Salas-Molina, Francisco
author_facet Salas-Molina, Francisco
Nin, Jordi
author_role author
author2 Nin, Jordi
author2_role author
dc.contributor.none.fl_str_mv Universitat Ramon Llull. Esade
dc.subject.none.fl_str_mv Hierarchical portfolio selection
Backbone extraction
Shrinkage covariance
Risk parity
topic Hierarchical portfolio selection
Backbone extraction
Shrinkage covariance
Risk parity
description Risk parity portfolio methods rely solely on covariance estimates to minimize risk, ignoring expected returns due to their high estimation error. This approach can be unstable when dealing with a reduced number of observations. We address this limitation by improving the signal-to-noise ratio in covariance and correlation matrix estimation within hierarchical portfolio selection models. Our approach combines shrinkage covariance estimation, a backbone network extraction, and density-based clustering method. We test two workflows: one for covariance and one for correlation matrices across four real-world market datasets (S&P, Dow Jones, Euro Stoxx 50, Ibex 35) and a synthetic dataset. Results show improved out-of-sample performance in terms of value-at-risk and conditional value-at-risk, offering a more robust alternative to standard hierarchical risk parity.
publishDate 2026
dc.date.none.fl_str_mv 2026
2026
2026
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.14342/6020
https://doi.org/10.1016/j.eswa.2025.130304
url http://hdl.handle.net/20.500.14342/6020
https://doi.org/10.1016/j.eswa.2025.130304
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Expert Systems with Applications, Vol. 299, Part D, 130304
dc.rights.none.fl_str_mv © L'autor/a
Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv © L'autor/a
Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 15 p.
application/pdf
dc.publisher.none.fl_str_mv Elsevier Ltd.
publisher.none.fl_str_mv Elsevier Ltd.
dc.source.none.fl_str_mv reponame:DAU Arxiu Digital de la Universitat Ramon Llull
instname:Universitat Ramon Llull (URL)
instname_str Universitat Ramon Llull (URL)
reponame_str DAU Arxiu Digital de la Universitat Ramon Llull
collection DAU Arxiu Digital de la Universitat Ramon Llull
repository.name.fl_str_mv
repository.mail.fl_str_mv
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