Compound vectors of subordinators and their associated positive L\'evy copulas
Abstract
L\'evy copulas are an important tool which can be used to build dependent L\'evy processes. In a classical setting, they have been used to model financial applications. In a Bayesian framework they have been employed to introduce dependent nonparametric priors which allow to model heterogeneous data. This paper focuses on introducing a new class of L\'evy copulas based on a class of subordinators recently appeared in the literature, called \textit{Compound Random Measures}. The well-known Clayton L\'evy copula is a special case of this new class. Furthermore, we provide some novel results about the underlying vector of subordinators such as a series representation and relevant moments. The article concludes with an application to a Danish fire dataset.
Keywords
Cite
@article{arxiv.1909.12112,
title = {Compound vectors of subordinators and their associated positive L\'evy copulas},
author = {Alan Riva Palacio and Fabrizio Leisen},
journal= {arXiv preprint arXiv:1909.12112},
year = {2020}
}