@article{Kim2013MixtureOD,
title={Mixture of D-vine copulas for modeling dependence},
author={Daeyoung Kim and Jong-Min Kim and Shu-Min Liao and Yoon-Sung Jung},
journal={Computational Statistics & Data Analysis},
year={2013},
volume={64},
pages={1-19}
}

The identification of an appropriate multivariate copula for capturing the dependence structure in multivariate data is not straightforward. The reason is because standard multivariate copulas (such as the multivariate Gaussian, Student-t, and exchangeable Archimedean copulas) lack flexibility to model dependence and have other limitations, such as parameter restrictions. To overcome these problems, vine copulas have been developed and applied to many applications. In order to reveal and fully… CONTINUE READING