Copula Density Estimation by Finite Mixture of Parametric Copula Densities

Leming Qu; Yang Lu

doi:10.1080/03610918.2019.1622720

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arXiv · 1906.09388

Copula Density Estimation by Finite Mixture of Parametric Copula Densities

Leming Qu·Yang Lu

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Abstract

A Copula density estimation method that is based on a finite mixture of heterogeneous parametric copula densities is proposed here. More specifically, the mixture components are Clayton, Frank, Gumbel, T, and normal copula densities, which are capable of capturing lower tail,strong central, upper tail, heavy tail, and symmetrical elliptical dependence, respectively. The model parameters are estimated by an interior-point algorithm for the constrained maximum likelihood problem. The interior-point algorithm is compared with the commonly used EM algorithm. Simulation and real data application show that the proposed approach is effective to model complex dependencies for data in dimensions beyond two or three.

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Leming Qu, Yang Lu. 2019-06-22. Copula Density Estimation by Finite Mixture of Parametric Copula Densities. https://doi.org/10.1080/03610918.2019.1622720

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