• Corpus ID: 126059013

Data driven partition-of-unity copulas with applications to risk management

  title={Data driven partition-of-unity copulas with applications to risk management},
  author={Dietmar Pfeifer and Andreas Mandle and Olena Ragulina},
  journal={arXiv: Risk Management},
We present a constructive and self-contained approach to data driven general partition-of-unity copulas that were recently introduced in the literature. In particular, we consider Bernstein-, negative binomial and Poisson copulas and present a solution to the problem of fitting such copulas to highly asymmetric data. 

Bayesian estimation of generalized partition of unity copulas

A Bayesian estimation algorithm to estimate Generalized Partition of Unity Copulas (GPUC), a class of nonparametric copulas recently introduced by [18], and presents an empirical illustration where GPUCs are used to nonparametrically describe the dependence of exchange rate changes of the crypto-currencies Bitcoin and Ethereum.



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

New multivariate copulas are constructed on the basis of a generalized infinite partition-of-unity approach that allows for tail-dependence as well as for asymmetry, in contrast to finite partition- of-unity copulas.

From Bernstein polynomials to Bernstein copulas

A pragmatic and effective way to fit the dependence structure of multivariate data to Bernstein copulas via rookCopulas, a subclass of checkerboard copulas, which is based on the multivariate empirical distribution is suggested.

An Introduction to Copulas. 2 nd Ed

  • 2007