Compound vectors of subordinators and their associated positive L\'evy copulas

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 $\mathit{\text{Compound}}\mathit{\text{Random}}\mathit{\text{Measures}}$. 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.