Impact of D-Vine Structure on Risk Estimation

In this paper, a sensitivity analysis using pair-copula decomposition of multivariate dependency models is performed on estimates of value-at-risk (VaR) and conditional value-at-risk (CVaR). To illustrate the results, we use four financial share portfolios selected to exemplify this purpose. For eac...

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Detalles Bibliográficos
Autores: Bolancé Losilla, Catalina, Alemany Leira, Ramon, Padilla Barreto, Alemar Elaine
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2018
País:España
Institución:Universidad de Barcelona
Repositorio:Dipòsit Digital de la UB
OAI Identifier:oai:diposit.ub.edu:2445/135398
Acceso en línea:https://hdl.handle.net/2445/135398
Access Level:acceso abierto
Palabra clave:Avaluació del risc
Risc (Economia)
Anàlisi multivariable
Risk assessment
Risk
Multivariate analysis
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spelling Impact of D-Vine Structure on Risk EstimationBolancé Losilla, CatalinaAlemany Leira, RamonPadilla Barreto, Alemar ElaineAvaluació del riscRisc (Economia)Anàlisi multivariableRisk assessmentRiskMultivariate analysisIn this paper, a sensitivity analysis using pair-copula decomposition of multivariate dependency models is performed on estimates of value-at-risk (VaR) and conditional value-at-risk (CVaR). To illustrate the results, we use four financial share portfolios selected to exemplify this purpose. For each share, we calculate filtered log returns using autoregressive moving average-generalized autoregressive conditional heteroscedasticity models and study their dependence. We analyze how selecting pairs of assets to define vines prior to pair-copula decomposition affects the estimated VaR and CVaR. Further, using bootstrap confidence intervals, we compare the results of different risk measures obtained by employing alternative measures of dependence to select the order in which the drawable vine (D-vine) is defined in different portfolios. Moreover, we carry out a simulation study to analyze the finite sample properties of the different criteria for selecting the pair-copula decomposition associated with the D-vine. We find some differences between the results obtained for VaR and CVaR.Incisive Media2018info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://hdl.handle.net/2445/135398Articles publicats en revistes (Econometria, Estadística i Economia Aplicada)reponame:Dipòsit Digital de la UBinstname:Universidad de BarcelonaInglésReproducció del document publicat a: https://doi.org/10.21314/JOR.2018.384Journal of Risk, 2018, vol. 20, num. 5, p. 1-32https://doi.org/10.21314/JOR.2018.384(c) Incisive Media, 2018info:eu-repo/semantics/openAccessoai:diposit.ub.edu:2445/1353982026-05-27T06:46:51Z
dc.title.none.fl_str_mv Impact of D-Vine Structure on Risk Estimation
title Impact of D-Vine Structure on Risk Estimation
spellingShingle Impact of D-Vine Structure on Risk Estimation
Bolancé Losilla, Catalina
Avaluació del risc
Risc (Economia)
Anàlisi multivariable
Risk assessment
Risk
Multivariate analysis
title_short Impact of D-Vine Structure on Risk Estimation
title_full Impact of D-Vine Structure on Risk Estimation
title_fullStr Impact of D-Vine Structure on Risk Estimation
title_full_unstemmed Impact of D-Vine Structure on Risk Estimation
title_sort Impact of D-Vine Structure on Risk Estimation
dc.creator.none.fl_str_mv Bolancé Losilla, Catalina
Alemany Leira, Ramon
Padilla Barreto, Alemar Elaine
author Bolancé Losilla, Catalina
author_facet Bolancé Losilla, Catalina
Alemany Leira, Ramon
Padilla Barreto, Alemar Elaine
author_role author
author2 Alemany Leira, Ramon
Padilla Barreto, Alemar Elaine
author2_role author
author
dc.subject.none.fl_str_mv Avaluació del risc
Risc (Economia)
Anàlisi multivariable
Risk assessment
Risk
Multivariate analysis
topic Avaluació del risc
Risc (Economia)
Anàlisi multivariable
Risk assessment
Risk
Multivariate analysis
description In this paper, a sensitivity analysis using pair-copula decomposition of multivariate dependency models is performed on estimates of value-at-risk (VaR) and conditional value-at-risk (CVaR). To illustrate the results, we use four financial share portfolios selected to exemplify this purpose. For each share, we calculate filtered log returns using autoregressive moving average-generalized autoregressive conditional heteroscedasticity models and study their dependence. We analyze how selecting pairs of assets to define vines prior to pair-copula decomposition affects the estimated VaR and CVaR. Further, using bootstrap confidence intervals, we compare the results of different risk measures obtained by employing alternative measures of dependence to select the order in which the drawable vine (D-vine) is defined in different portfolios. Moreover, we carry out a simulation study to analyze the finite sample properties of the different criteria for selecting the pair-copula decomposition associated with the D-vine. We find some differences between the results obtained for VaR and CVaR.
publishDate 2018
dc.date.none.fl_str_mv 2018
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/2445/135398
url https://hdl.handle.net/2445/135398
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Reproducció del document publicat a: https://doi.org/10.21314/JOR.2018.384
Journal of Risk, 2018, vol. 20, num. 5, p. 1-32
https://doi.org/10.21314/JOR.2018.384
dc.rights.none.fl_str_mv (c) Incisive Media, 2018
info:eu-repo/semantics/openAccess
rights_invalid_str_mv (c) Incisive Media, 2018
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Incisive Media
publisher.none.fl_str_mv Incisive Media
dc.source.none.fl_str_mv Articles publicats en revistes (Econometria, Estadística i Economia Aplicada)
reponame:Dipòsit Digital de la UB
instname:Universidad de Barcelona
instname_str Universidad de Barcelona
reponame_str Dipòsit Digital de la UB
collection Dipòsit Digital de la UB
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,301629

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