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Explicit solutions to correlation matrix completion problems, with an application to risk management and insurance

We derive explicit solutions to the problem of completing a partially specified correlation matrix. Our results apply to several block structures for the unspecified entries that arise in insurance and risk management, where an insurance company with many lines of business is required to satisfy cer...

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Detalles Bibliográficos
Autores principales: Georgescu, Dan I., Higham, Nicholas J., Peters, Gareth W.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society Publishing 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5882745/
https://www.ncbi.nlm.nih.gov/pubmed/29657821
http://dx.doi.org/10.1098/rsos.172348
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author Georgescu, Dan I.
Higham, Nicholas J.
Peters, Gareth W.
author_facet Georgescu, Dan I.
Higham, Nicholas J.
Peters, Gareth W.
author_sort Georgescu, Dan I.
collection PubMed
description We derive explicit solutions to the problem of completing a partially specified correlation matrix. Our results apply to several block structures for the unspecified entries that arise in insurance and risk management, where an insurance company with many lines of business is required to satisfy certain capital requirements but may have incomplete knowledge of the underlying correlation matrix. Among the many possible completions, we focus on the one with maximal determinant. This has attractive properties and we argue that it is suitable for use in the insurance application. Our explicit formulae enable easy solution of practical problems and are useful for testing more general algorithms for the maximal determinant correlation matrix completion problem.
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spelling pubmed-58827452018-04-13 Explicit solutions to correlation matrix completion problems, with an application to risk management and insurance Georgescu, Dan I. Higham, Nicholas J. Peters, Gareth W. R Soc Open Sci Mathematics We derive explicit solutions to the problem of completing a partially specified correlation matrix. Our results apply to several block structures for the unspecified entries that arise in insurance and risk management, where an insurance company with many lines of business is required to satisfy certain capital requirements but may have incomplete knowledge of the underlying correlation matrix. Among the many possible completions, we focus on the one with maximal determinant. This has attractive properties and we argue that it is suitable for use in the insurance application. Our explicit formulae enable easy solution of practical problems and are useful for testing more general algorithms for the maximal determinant correlation matrix completion problem. The Royal Society Publishing 2018-03-14 /pmc/articles/PMC5882745/ /pubmed/29657821 http://dx.doi.org/10.1098/rsos.172348 Text en © 2018 The Authors. http://creativecommons.org/licenses/by/4.0/ Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.
spellingShingle Mathematics
Georgescu, Dan I.
Higham, Nicholas J.
Peters, Gareth W.
Explicit solutions to correlation matrix completion problems, with an application to risk management and insurance
title Explicit solutions to correlation matrix completion problems, with an application to risk management and insurance
title_full Explicit solutions to correlation matrix completion problems, with an application to risk management and insurance
title_fullStr Explicit solutions to correlation matrix completion problems, with an application to risk management and insurance
title_full_unstemmed Explicit solutions to correlation matrix completion problems, with an application to risk management and insurance
title_short Explicit solutions to correlation matrix completion problems, with an application to risk management and insurance
title_sort explicit solutions to correlation matrix completion problems, with an application to risk management and insurance
topic Mathematics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5882745/
https://www.ncbi.nlm.nih.gov/pubmed/29657821
http://dx.doi.org/10.1098/rsos.172348
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