Quantifying the risk using copulae with nonparametric marginals

Catalina Bolancé, Zuhair Bahraoui and Manuel Artís

Insurance: Mathematics and Economics, 2014, vol. 58, issue C, 46-56

Abstract: We show that copulae and kernel estimation can be mixed to estimate the risk of an economic loss. We analyze the properties of the Sarmanov copula. We find that the maximum pseudo-likelihood estimation of the dependence parameter associated with the copula with double transformed kernel estimation to estimate marginal cumulative distribution functions is a useful method for approximating the risk of extreme dependent losses when we have large data sets. We use a bivariate sample of losses from a real database of auto insurance claims.

Keywords: Extreme value copula; Tail dependence; Sarmanov copula; Nonparametric marginals; Value-at-risk (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:insuma:v:58:y:2014:i:c:p:46-56

DOI: 10.1016/j.insmatheco.2014.06.008

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