Modelling Dependence of Insurance Risks

Modelling one-dimensional data can be performed by different wellknown ways. Modelling two-dimensional data is a more open question. There is no unique way to describe dependency of two dimensional data. In this thesis dependency is modelled by copulas. Insurance data from two different regions (Göinge and Kronoberg) in Southern Sweden is investigated. It is found that a suitable model is that marginal data are Normal Inverse Gaussian distributed and copula is a better dependence measure than the usual linear correlation together with Gaussian marginals.