Artikel
Modeling bivariate dependency in insurance data via Copula: A brief study
Copulas are a quite flexible and useful tool for modeling the dependence structure between two or more variables or components of bivariate and multivariate vectors, in particular, to predict losses in insurance and finance. In this article, we use the VineCopula package in R to study the dependence structure of some well-known real-life insurance data and identify the best bivariate copula in each case. Associated structural properties of these bivariate copulas are also discussed with a major focus on their tail dependence structure. This study shows that certain types of Archimedean copula with the heavy tail dependence property are a reasonable framework to start in terms modeling insurance claim data both in the bivariate as well as in the case of multivariate domains as appropriate.
- Language
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Englisch
- Bibliographic citation
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Journal: Journal of Risk and Financial Management ; ISSN: 1911-8074 ; Volume: 15 ; Year: 2022 ; Issue: 8 ; Pages: 1-20
- Classification
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Management
- Subject
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bivariate copula
Blomqvist’
s Ø
dependence modeling
Kendall’
s τ
measures of association
- Event
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Geistige Schöpfung
- (who)
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Ghosh, Indranil
Watts, Dalton
Chakraborty, Subrata
- Event
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Veröffentlichung
- (who)
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MDPI
- (where)
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Basel
- (when)
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2022
- DOI
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doi:10.3390/jrfm15080329
- Handle
- Last update
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10.03.2025, 11:43 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Artikel
Associated
- Ghosh, Indranil
- Watts, Dalton
- Chakraborty, Subrata
- MDPI
Time of origin
- 2022
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