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An incremental loss ratio method using prior information on calendar year effects

In a run-off triangle external factors can have a similar influence on all incremental losses of the same calendar year. This can distort the triangle such that reserving methods like chain ladder or the loss ratio method do not work properly. A very recent example of such an external factor is the...

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Autor principal: Riegel, Ulrich
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9119846/
https://www.ncbi.nlm.nih.gov/pubmed/35611236
http://dx.doi.org/10.1007/s13385-022-00315-3
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author Riegel, Ulrich
author_facet Riegel, Ulrich
author_sort Riegel, Ulrich
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description In a run-off triangle external factors can have a similar influence on all incremental losses of the same calendar year. This can distort the triangle such that reserving methods like chain ladder or the loss ratio method do not work properly. A very recent example of such an external factor is the Covid-19 pandemic. In many countries, the insurance industry is in the process of establishing market knowledge about the impact of the pandemic on premiums and losses. We extend the additive claims reserving model to allow for calendar year effects and develop a variant of the incremental loss ratio method (also known as the additive method) that can make use of such market knowledge. We derive formulas for the mean squared error of prediction and provide a detailed numerical example. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13385-022-00315-3.
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spelling pubmed-91198462022-05-20 An incremental loss ratio method using prior information on calendar year effects Riegel, Ulrich Eur Actuar J Original Research Paper In a run-off triangle external factors can have a similar influence on all incremental losses of the same calendar year. This can distort the triangle such that reserving methods like chain ladder or the loss ratio method do not work properly. A very recent example of such an external factor is the Covid-19 pandemic. In many countries, the insurance industry is in the process of establishing market knowledge about the impact of the pandemic on premiums and losses. We extend the additive claims reserving model to allow for calendar year effects and develop a variant of the incremental loss ratio method (also known as the additive method) that can make use of such market knowledge. We derive formulas for the mean squared error of prediction and provide a detailed numerical example. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13385-022-00315-3. Springer Berlin Heidelberg 2022-05-20 2023 /pmc/articles/PMC9119846/ /pubmed/35611236 http://dx.doi.org/10.1007/s13385-022-00315-3 Text en © EAJ Association 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Original Research Paper
Riegel, Ulrich
An incremental loss ratio method using prior information on calendar year effects
title An incremental loss ratio method using prior information on calendar year effects
title_full An incremental loss ratio method using prior information on calendar year effects
title_fullStr An incremental loss ratio method using prior information on calendar year effects
title_full_unstemmed An incremental loss ratio method using prior information on calendar year effects
title_short An incremental loss ratio method using prior information on calendar year effects
title_sort incremental loss ratio method using prior information on calendar year effects
topic Original Research Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9119846/
https://www.ncbi.nlm.nih.gov/pubmed/35611236
http://dx.doi.org/10.1007/s13385-022-00315-3
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