Modeling Bivariate Dependency in Insurance Data via Copula: A Brief Study

Indranil Ghosh, Dalton Watts and Subrata Chakraborty
Additional contact information
Indranil Ghosh: Department of Mathematics and Statistics, University of North Carolina, Wilmington, NC 28403, USA
Dalton Watts: Department of Mathematics and Statistics, University of North Carolina, Wilmington, NC 28403, USA
Subrata Chakraborty: Department of Statistics, Dibrugarh University, Assam 786004, India

JRFM, 2022, vol. 15, issue 8, 1-20

Abstract: 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.

Keywords: bivariate copula; measures of association; dependence modeling; Kendall’s ?; Blomqvist’s ? (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1911-8074/15/8/329/pdf (application/pdf)
https://www.mdpi.com/1911-8074/15/8/329/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jjrfmx:v:15:y:2022:i:8:p:329-:d:870933

Access Statistics for this article

JRFM is currently edited by Ms. Chelthy Cheng

More articles in JRFM from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().