Hierarchical Kendall copulas: Properties and inference

Details Hierarchical Kendall copulas: Properties and inference Hierarchical Kendall copulas: Properties and inference

2012-02-09

arxiv.org/abs/1202.1998v2

Statistics

Methodology

Scientific paper

While there is substantial need for dependence models in high dimensions, most existing models strongly suffer from the curse of dimensionality and barely balance parsimony and flexibility. In this paper, the new class of hierarchical Kendall copulas is proposed which tackles these problems. Constructed with flexible copulas specified for groups of variables in different hierarchical levels, hierarchical Kendall copulas are able to model complex dependence patterns without severe restrictions. The paper explicitly discusses inference techniques for hierarchical Kendall copulas, in particular, simulation, estimation and model selection. A substantive application to German stock returns finally shows that hierarchical Kendall copulas perform very well, out-of- as well as in-sample.

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