2019/09/26 by Alan Riva Palacio, Fabrizio Leisen, Palacio, Alan Riva +1
Computer Science · Economics, Econometrics and Finance · Mathematics · Social Sciences · #Bayesian Methods and Mixture Models #FOS: Computer and information sciences #FOS: Mathematics #Financial Risk and Volatility Modeling #Insurance, Mortality, Demography, Risk Management #Methodology (stat.ME) #Probability (math.PR) #Statistics Theory (math.ST) #Stochastic processes and financial applications #math.PR #math.ST #stat.ME #stat.TH
paper · pdf · doi:10.48550/arxiv.1909.12112
openalex publication_date 2019/09/26 · arxiv created 2020/08/31 · arxiv updated 2020/09/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.