Extending one-factor copulas
Methodology
2016-12-12 v1
Abstract
So far, one-factor copulas induce conditional independence with respect to a latent factor. In this paper, we extend one-factor copulas to conditionally dependent models. This is achieved through new representations which allow to build new parametric factor copulas with a varying conditional dependence structure. We discuss estimation and properties of these representations. In order to distinguish between conditionally independent and conditionally dependent factor copulas, we provide a novel statistical test which does not assume any parametric form for the conditional dependence structure. Illustrations of our framework are provided through examples, numerical experiments, as well as a real data analysis.
Cite
@article{arxiv.1612.02848,
title = {Extending one-factor copulas},
author = {Nathan Uyttendaele and Gildas Mazo},
journal= {arXiv preprint arXiv:1612.02848},
year = {2016}
}