The Cuadras-Auge copula is a well-known model for continuous-discrete data. However, it has restrictions due to it’s range of dependence which only covers positive dependence and has pure upper tail dependence. In this talk, an Extension of Cuadras-Auge copula will be introduced by extending the dependence structure and tail dependence making it more exible for modelling. Properties useful for modelling such as association measures, tail dependence, Kendall distribution, concavity and invariancy has been presented followed by a statistical interpretation which results a stochastic comparison and concludes an ordering that is applied in actuarial premium principle.

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