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

TL;DR

This paper introduces a new method for constructing data-driven infinite partition-of-unity copulas, including negative binomial and Poisson types, with applications to modeling highly asymmetric multivariate data in risk management.

Contribution

It provides a constructive, self-contained approach to fit complex copulas to asymmetric data, expanding the toolkit for multivariate risk modeling.

Findings

Effective fitting of negative binomial and Poisson copulas to asymmetric data

Applicable to high-dimensional risk management scenarios

Enhanced flexibility in modeling complex dependence structures

Abstract

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.