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Risk-Adjusted Estimation and Graduation of Transition Intensities for Disability and Long-Term Care Insurance

2025-07Artigo científico Acesso aberto
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dc.contributor.authorCurioso, Beatriz A.
dc.contributor.authorGuerreiro, Gracinda R.
dc.contributor.authorEsquível, Manuel L.
dc.contributor.institutionDM - Departamento de Matemática
dc.contributor.institutionCMA - Centro de Matemática e Aplicações
dc.contributor.institutionFaculdade de Ciências e Tecnologia (FCT)
dc.contributor.pblMDPI - Multidisciplinary Digital Publishing Institute
dc.date.accessioned2025-09-04T22:05:23Z
dc.date.available2025-09-04T22:05:23Z
dc.date.issued2025-07
dc.descriptionCopyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland.
dc.description.abstractThis paper introduces a methodology for estimating transition intensities in a multi-state model for disability and long-term care insurance. We propose a novel framework that integrates observable risk factors, such as demographic (age and sex), lifestyle (smoking and exercise habits) and health-related variables (body mass index), into the estimation and graduation of transition intensities, using a parametric approach based on the Gompertz–Makeham law and generalised linear models. The model features four states—autonomous, dead, and two intermediate states representing varying disability levels—providing a detailed view of disability/lack of autonomy progression. To illustrate the proposed framework, we simulate a dataset with individual risk profiles and model trajectories, mirroring Portugal’s demographic composition. This allows us to derive a functional form (as a function of age) for the transition intensities, stratified by relevant risk factors, thus enabling precise risk differentiation. The results offer a robust basis for developing tailored pricing structures in the Portuguese market, with broader applications in actuarial science and insurance. By combining granular disability modelling with risk factor integration, our approach enhances accuracy in pricing structure and risk assessment.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent651173
dc.identifier.doi10.3390/risks13070124
dc.identifier.issn2227-9091
dc.identifier.otherPURE: 128562914
dc.identifier.otherPURE UUID: 2967d93d-c005-4696-a679-677664b8640e
dc.identifier.otherScopus: 105011646914
dc.identifier.urihttp://hdl.handle.net/10362/187543
dc.identifier.urlhttps://www.scopus.com/pages/publications/105011646914
dc.language.isoeng
dc.peerreviewedyes
dc.relationFunding Information: info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00297%2F2020/PT
dc.relationinfo:eu-repo/grantAgreement/FCT/Concurso de avaliação no âmbito do Programa Plurianual de Financiamento de Unidades de I&D (2017%2F2018) - Financiamento Programático/UIDP%2F00297%2F2020/PT
dc.subjectdata simulation
dc.subjectdisability insurance
dc.subjectgraduation
dc.subjectlong-term care
dc.subjectmulti-state models
dc.subjecttransition intensity approach
dc.subjectAccounting
dc.subjectEconomics, Econometrics and Finance (miscellaneous)
dc.subjectStrategy and Management
dc.titleRisk-Adjusted Estimation and Graduation of Transition Intensities for Disability and Long-Term Care Insuranceen
dc.title.subtitleA Multi-State Model Approachen
dc.typejournal article
degois.publication.issue7
degois.publication.titleRisks
degois.publication.volume13
dspace.entity.typePublication
oaire.awardNumberUIDP/00297/2020
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/Concurso de avaliação no âmbito do Programa Plurianual de Financiamento de Unidades de I&D (2017%2F2018) - Financiamento Programático/UIDP%2F00297%2F2020/PT
oaire.fundingStreamConcurso de avaliação no âmbito do Programa Plurianual de Financiamento de Unidades de I&D (2017/2018) - Financiamento Programático
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccess
relation.isProjectOfPublication6e1dd6d8-b858-4ef7-83c5-1571ac7def8e
relation.isProjectOfPublication.latestForDiscovery6e1dd6d8-b858-4ef7-83c5-1571ac7def8e

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