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 understand the complex and hidden dependence patterns in multivariate data, a mixture of D-vine copulas is proposed incorporating D-vine copulas into a finite mixture model. As a D-vine copula has multiple parameters capturing the dependence through iterative construction of pair-copulas, the proposed model can facilitate a comprehensive study of complex and hidden dependence patterns in multivariate data. The proposed mixture of D-vine copulas is applied to simulated and real data to illustrate its performance and benefits.
