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New copulas based on general partitions-of-unity and their applications to risk management (part II)

Dietmar Pfeifer;  Andreas Mändle;  Olena Ragulina — 2017

Dependence Modeling

We present a constructive and self-contained approach to data driven infinite partition-of-unity copulas that were recently introduced in the literature. In particular, we consider negative binomial and Poisson copulas and present a solution to the problem of fitting such copulas to highly asymmetric data in arbitrary dimensions.

New copulas based on general partitions-of-unity and their applications to risk management

Dietmar Pfeifer;  Hervé Awoumlac Tsatedem;  Andreas Mändle;  Côme Girschig — 2016

Dependence Modeling

We construct new multivariate copulas on the basis of a generalized infinite partition-of-unity approach. This approach allows, in contrast to finite partition-of-unity copulas, for tail-dependence as well as for asymmetry. A possibility of fitting such copulas to real data from quantitative risk management is also pointed out.

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