Performance comparison of scenario-generation methods applied to a stochastic optimization asset-liability management model

Detalhes bibliográficos
Autor(a) principal: Oliveira, Alan Delgado de
Data de Publicação: 2018
Outros Autores: Filomena, Tiago Pascoal, Righi, Marcelo Brutti
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UFRGS
Texto Completo: http://hdl.handle.net/10183/175136
Resumo: In this paper, we provide an empirical discussion of the differences among some scenario tree-generation approaches for stochastic programming. We consider the classical Monte Carlo sampling and Moment matching methods. Moreover, we test the Resampled average approximation, which is an adaptation of Monte Carlo sampling and Monte Carlo with naive allocation strategy as the benchmark. We test the empirical effects of each approach on the stability of the problem objective function and initial portfolio allocation, using a multistage stochastic chance-constrained asset-liability management (ALM) model as the application. The Moment matching and Resampled average approximation are more stable than the other two strategies.
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spelling Oliveira, Alan Delgado deFilomena, Tiago PascoalRighi, Marcelo Brutti2018-04-27T02:43:15Z20180101-7438http://hdl.handle.net/10183/175136001065937In this paper, we provide an empirical discussion of the differences among some scenario tree-generation approaches for stochastic programming. We consider the classical Monte Carlo sampling and Moment matching methods. Moreover, we test the Resampled average approximation, which is an adaptation of Monte Carlo sampling and Monte Carlo with naive allocation strategy as the benchmark. We test the empirical effects of each approach on the stability of the problem objective function and initial portfolio allocation, using a multistage stochastic chance-constrained asset-liability management (ALM) model as the application. The Moment matching and Resampled average approximation are more stable than the other two strategies.application/pdfengPesquisa operacional. Rio de Janeiro. Vol. 38, n.1 (2018), p. 53-72Modelo de gestãoOtimização estocásticaProgramacao estocasticaScenario generationStochastic programingMultistageALMPerformance comparison of scenario-generation methods applied to a stochastic optimization asset-liability management modelinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/otherinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFRGSinstname:Universidade Federal do Rio Grande do Sul (UFRGS)instacron:UFRGSORIGINAL001065937.pdf001065937.pdfTexto completo (inglês)application/pdf222761http://www.lume.ufrgs.br/bitstream/10183/175136/1/001065937.pdf1da3f7a54eb9f64c1e6935e3a76ff634MD51TEXT001065937.pdf.txt001065937.pdf.txtExtracted Texttext/plain57663http://www.lume.ufrgs.br/bitstream/10183/175136/2/001065937.pdf.txt7d7a4296c07171171efad7beef6e608bMD52THUMBNAIL001065937.pdf.jpg001065937.pdf.jpgGenerated Thumbnailimage/jpeg1544http://www.lume.ufrgs.br/bitstream/10183/175136/3/001065937.pdf.jpg1ebb58ff16e68851a0fd50e1a32941a1MD5310183/1751362018-10-25 10:10:55.01oai:www.lume.ufrgs.br:10183/175136Repositório de PublicaçõesPUBhttps://lume.ufrgs.br/oai/requestopendoar:2018-10-25T13:10:55Repositório Institucional da UFRGS - Universidade Federal do Rio Grande do Sul (UFRGS)false
dc.title.pt_BR.fl_str_mv Performance comparison of scenario-generation methods applied to a stochastic optimization asset-liability management model
title Performance comparison of scenario-generation methods applied to a stochastic optimization asset-liability management model
spellingShingle Performance comparison of scenario-generation methods applied to a stochastic optimization asset-liability management model
Oliveira, Alan Delgado de
Modelo de gestão
Otimização estocástica
Programacao estocastica
Scenario generation
Stochastic programing
Multistage
ALM
title_short Performance comparison of scenario-generation methods applied to a stochastic optimization asset-liability management model
title_full Performance comparison of scenario-generation methods applied to a stochastic optimization asset-liability management model
title_fullStr Performance comparison of scenario-generation methods applied to a stochastic optimization asset-liability management model
title_full_unstemmed Performance comparison of scenario-generation methods applied to a stochastic optimization asset-liability management model
title_sort Performance comparison of scenario-generation methods applied to a stochastic optimization asset-liability management model
author Oliveira, Alan Delgado de
author_facet Oliveira, Alan Delgado de
Filomena, Tiago Pascoal
Righi, Marcelo Brutti
author_role author
author2 Filomena, Tiago Pascoal
Righi, Marcelo Brutti
author2_role author
author
dc.contributor.author.fl_str_mv Oliveira, Alan Delgado de
Filomena, Tiago Pascoal
Righi, Marcelo Brutti
dc.subject.por.fl_str_mv Modelo de gestão
Otimização estocástica
Programacao estocastica
topic Modelo de gestão
Otimização estocástica
Programacao estocastica
Scenario generation
Stochastic programing
Multistage
ALM
dc.subject.eng.fl_str_mv Scenario generation
Stochastic programing
Multistage
ALM
description In this paper, we provide an empirical discussion of the differences among some scenario tree-generation approaches for stochastic programming. We consider the classical Monte Carlo sampling and Moment matching methods. Moreover, we test the Resampled average approximation, which is an adaptation of Monte Carlo sampling and Monte Carlo with naive allocation strategy as the benchmark. We test the empirical effects of each approach on the stability of the problem objective function and initial portfolio allocation, using a multistage stochastic chance-constrained asset-liability management (ALM) model as the application. The Moment matching and Resampled average approximation are more stable than the other two strategies.
publishDate 2018
dc.date.accessioned.fl_str_mv 2018-04-27T02:43:15Z
dc.date.issued.fl_str_mv 2018
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dc.identifier.uri.fl_str_mv http://hdl.handle.net/10183/175136
dc.identifier.issn.pt_BR.fl_str_mv 0101-7438
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001065937
url http://hdl.handle.net/10183/175136
dc.language.iso.fl_str_mv eng
language eng
dc.relation.ispartof.pt_BR.fl_str_mv Pesquisa operacional. Rio de Janeiro. Vol. 38, n.1 (2018), p. 53-72
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
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