The bivariate K-finite normal mixture ‘blanket’ copula

There exist many bivariate parametric copulas to model data with different dependence features. We propose a new copula family that cannot only handle various patterns appear in the existing families, but also provides more enriched structure. The proposed construction exploits finite mixtures of normal distributions. mixing operation, distinct correlation and mean parameters at each mixture component introduce quite flexible dependence. is theoretically investigated, compared set classical illustrated on two empirical examples from astrophysics agriculture where some variables have peculiar asymmetric dependence, respectively.