Quantifying the risk using copulae with nonparametric marginals
Affiliation:
1. Faculty of Civil Engineering, Universiti Teknologi Malaysia (UTM), 81310 UTM Johor Bahru, Johor, Malaysia;2. Construction Research Centre, Universiti Teknologi Malaysia (UTM-CRC), 81310 UTM Johor Bahru, Johor, Malaysia
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.