Compound vectors of subordinators and their associated positive Lévy copulas

Alan Riva-Palacio and Fabrizio Leisen

Journal of Multivariate Analysis, 2021, vol. 183, issue C

Abstract: Lévy copulas are an important tool which can be used to build dependent Lévy 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 Lévy copulas based on a class of subordinators recently appeared in the literature, called compound random measures. The well-known Clayton Lévy 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: Clayton Lévy copulas; Dependent completely random measures; Lévy processes (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1016/j.jmva.2021.104728

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