Abstract
In this article, we review the concept of a Lévy copula to describe the dependence structure of a bivariate compound Poisson process. In this first statistical approach we consider a parametric model for the Lévy copula and estimate the parameters of the full dependent model based on a maximum likelihood approach. This approach ensures that the estimated model remains in the class of multivariate compound Poisson processes. A simulation study investigates the small sample behaviour of the MLEs, where we also suggest a new simulation algorithm. Finally, we apply our method to Danish fire insurance data.
| Original language | English |
|---|---|
| Pages (from-to) | 224-233 |
| Number of pages | 10 |
| Journal | Insurance: Mathematics and Economics |
| Volume | 47 |
| Issue number | 2 |
| DOIs | |
| State | Published - Oct 2010 |
Keywords
- Dependence modelling
- Lévy copula
- Lévy measure
- Lévy process
- Maximum likelihood estimation
- Multivariate compound Poisson process
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