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There exist many bivariate parametric copulas to model bivariate data withdifferent dependence features. We propose a new bivariate parametric copulafamily that cannot only handle various dependence patterns that appear in theexisting parametric bivariate copula families, but also provides a moreenriched dependence structure. The proposed copula construction exploits finitemixtures of bivariate normal distributions. The mixing operation, the distinctcorrelation and mean parameters at each mixture component introduce quite aflexible dependence. The new parametric copula is theoretically investigated,compared with a set of classical bivariate parametric copulas and illustratedon two empirical examples from astrophysics and agriculture where some of thevariables have peculiar and asymmetric dependence, respectively.

The bivariate $K$-finite normal mixture "blanket" copula