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Fitting High-Dimensional Copulae to Data

Chapter
Part of the Springer Handbooks of Computational Statistics book series (SHCS)

Abstract

This paper make an overview of the copula theory from a practical side. We consider different methods of copula estimation and different Goodness-of-Fit tests for model selection. In the GoF section we apply Kolmogorov-Smirnov and Cramer-von-Mises type tests and calculate power of these tests under different assumptions. Novating in this paper is that all the procedures are done in dimensions higher than two, and in comparison to other papers we consider not only simple Archimedean and Gaussian copulae but also Hierarchical Archimedean Copulae. Afterwards we provide an empirical part to support the theory.

Keywords

Marginal Distribution Multivariate Distribution Copula Function Copula Model Gaussian Copula 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

The financial support from the Deutsche Forschungsgemeinschaft through SFB 649 “Ökonomisches Risiko”, Humboldt-Universität zu Berlin is gratefully acknowledged.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Ladislaus von Bortkiewitcz Chair of Statistics, C.A.S.E. – Center of Applied Statistics and EconomicsHumboldt-Universität zu BerlinBerlinGermany

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