Portfolio selection under uncertainty: a new methodology for computing relative‐robust solutions
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10316/87194 https://doi.org/10.1111/itor.12674 |
Resumo: | In this paper, a new methodology for computing relative-robust portfolios based on minimax regret is proposed. Regret is defined as the utility loss for the investor resulting from choosing a given portfolio instead of choosing the optimal portfolio of the realized scenario. The absolute robust strategy was also considered and, in this case, the minimum investor’s expected utility in the worst-case scenario is maximized. Several subsamples are gathered from the in-sample data and for each subsample a minimax regret and a maximin solution are computed, to avoid the risk of overfitting. Robust portfolios are computed using a genetic algorithm, allowing the transformation of a 3-level optimization problem in a 2-level problem. Results show that the proposed relative-robust portfolio generally outperforms (other) relative-robust and non-robust portfolios, except for the global minimum variance portfolio. Furthermore, the relative-robust portfolio generally outperforms the absolute-robust portfolio, even considering higher risk aversion levels. |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Portfolio selection under uncertainty: a new methodology for computing relative‐robust solutionsRobust optimizationportfolio selectionrelative robustnessminimax regretIn this paper, a new methodology for computing relative-robust portfolios based on minimax regret is proposed. Regret is defined as the utility loss for the investor resulting from choosing a given portfolio instead of choosing the optimal portfolio of the realized scenario. The absolute robust strategy was also considered and, in this case, the minimum investor’s expected utility in the worst-case scenario is maximized. Several subsamples are gathered from the in-sample data and for each subsample a minimax regret and a maximin solution are computed, to avoid the risk of overfitting. Robust portfolios are computed using a genetic algorithm, allowing the transformation of a 3-level optimization problem in a 2-level problem. Results show that the proposed relative-robust portfolio generally outperforms (other) relative-robust and non-robust portfolios, except for the global minimum variance portfolio. Furthermore, the relative-robust portfolio generally outperforms the absolute-robust portfolio, even considering higher risk aversion levels.Wiley2019-04-29info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10316/87194http://hdl.handle.net/10316/87194https://doi.org/10.1111/itor.12674eng1475-3995https://doi.org/10.1111/itor.12674Caçador, SandraDias, Joana MatosGodinho, Pedroinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2021-08-24T08:48:24Zoai:estudogeral.uc.pt:10316/87194Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T21:08:10.189695Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Portfolio selection under uncertainty: a new methodology for computing relative‐robust solutions |
title |
Portfolio selection under uncertainty: a new methodology for computing relative‐robust solutions |
spellingShingle |
Portfolio selection under uncertainty: a new methodology for computing relative‐robust solutions Caçador, Sandra Robust optimization portfolio selection relative robustness minimax regret |
title_short |
Portfolio selection under uncertainty: a new methodology for computing relative‐robust solutions |
title_full |
Portfolio selection under uncertainty: a new methodology for computing relative‐robust solutions |
title_fullStr |
Portfolio selection under uncertainty: a new methodology for computing relative‐robust solutions |
title_full_unstemmed |
Portfolio selection under uncertainty: a new methodology for computing relative‐robust solutions |
title_sort |
Portfolio selection under uncertainty: a new methodology for computing relative‐robust solutions |
author |
Caçador, Sandra |
author_facet |
Caçador, Sandra Dias, Joana Matos Godinho, Pedro |
author_role |
author |
author2 |
Dias, Joana Matos Godinho, Pedro |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Caçador, Sandra Dias, Joana Matos Godinho, Pedro |
dc.subject.por.fl_str_mv |
Robust optimization portfolio selection relative robustness minimax regret |
topic |
Robust optimization portfolio selection relative robustness minimax regret |
description |
In this paper, a new methodology for computing relative-robust portfolios based on minimax regret is proposed. Regret is defined as the utility loss for the investor resulting from choosing a given portfolio instead of choosing the optimal portfolio of the realized scenario. The absolute robust strategy was also considered and, in this case, the minimum investor’s expected utility in the worst-case scenario is maximized. Several subsamples are gathered from the in-sample data and for each subsample a minimax regret and a maximin solution are computed, to avoid the risk of overfitting. Robust portfolios are computed using a genetic algorithm, allowing the transformation of a 3-level optimization problem in a 2-level problem. Results show that the proposed relative-robust portfolio generally outperforms (other) relative-robust and non-robust portfolios, except for the global minimum variance portfolio. Furthermore, the relative-robust portfolio generally outperforms the absolute-robust portfolio, even considering higher risk aversion levels. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-04-29 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10316/87194 http://hdl.handle.net/10316/87194 https://doi.org/10.1111/itor.12674 |
url |
http://hdl.handle.net/10316/87194 https://doi.org/10.1111/itor.12674 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
1475-3995 https://doi.org/10.1111/itor.12674 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Wiley |
publisher.none.fl_str_mv |
Wiley |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799133973630156800 |