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Compound vectors of subordinators and their associated positive Lévy copulas

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  • Riva-Palacio, Alan
  • Leisen, Fabrizio

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.

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  • Riva-Palacio, Alan & Leisen, Fabrizio, 2021. "Compound vectors of subordinators and their associated positive Lévy copulas," Journal of Multivariate Analysis, Elsevier, vol. 183(C).
    • Handle: RePEc:eee:jmvana:v:183:y:2021:i:c:s0047259x21000063
    DOI: 10.1016/j.jmva.2021.104728
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    References listed on IDEAS

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    Cited by:

    1. Marta Catalano & Claudio Del Sole & Antonio Lijoi & Igor Prünster, 2024. "A Unified Approach to Hierarchical Random Measures," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 86(1), pages 255-287, November.

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