A Mixture of Clayton, Gumbel, and Frank Copulas: A Complete Dependence Model

Knowledge of the dependence between random variables is necessary in area risk assessment and evaluation. Some existing Archimedean copulas, namely Clayton Gumbel allow for higher correlations on extreme left right, respectively. In this study, we use idea convex combinations to build a hybrid Clayton–Gumbel–Frank copula that provides all scenarios from copulas. The corresponding density conditional distribution functions derived models two variables, as well an estimator proportion parameter associated with proposed model, are also derived. results show model able any case by providing coefficients upper tail lower dependence.