Preprint Article Version 1 This version is not peer-reviewed

Statistical Modeling of Insurance Data via Vine Copula

Version 1 : Received: 22 June 2019 / Approved: 24 June 2019 / Online: 24 June 2019 (08:58:06 CEST)

How to cite: GHOSH, I.; Watts, D. Statistical Modeling of Insurance Data via Vine Copula. Preprints 2019, 2019060235 (doi: 10.20944/preprints201906.0235.v1). GHOSH, I.; Watts, D. Statistical Modeling of Insurance Data via Vine Copula. Preprints 2019, 2019060235 (doi: 10.20944/preprints201906.0235.v1).

Abstract

Copulas are useful tools for modeling the dependence structure between two or more variables. Copulas are becoming a quite flexible tool in modeling dependence among the components of a multivariate vector, in particular to predict losses in insurance and finance. In this article, we study the dependence structure of some well-known real life insurance data (with two components mainly) and subsequently identify the best bivariate copula to model such a scenario via VineCopula package in R. Associated structural properties of these bivariate copulas are also discussed.

Subject Areas

bivariate Copula; measures of association; dependence modeling; Kendall’s t; Blomqvist’s P

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