On the structure and estimation of hierarchical Archimedean copulas
By Ostap Okhrin, Yarema Okhrin, Wolfgang Schmid. 2013
159 citations recorded by the source corpus.
In this paper we provide a method for estimating multivariate distributions defined through hierarchical Archimedean copulas. In general, the true structure of the hierarchy is unknown, but we develop a computationally efficient technique to determine it from the data. For this purpose we introduce a hierarchical estimation procedure for the parameters and provide an asymptotic analysis. We consider both parametric and nonparametric estimation of the marginal distributions. A simulation study and an empirical application show the effectiveness of the grouping procedure in the sense of structure selection.
Subfields: Artificial Intelligence, Economics and Econometrics, Finance
View Paper: https://www.semanticscholar.org/paper/eb48cf71cf8792ffd99f411ac0d88b6f07875ca1
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Citations in the corpus
- An Overview of the Goodness-of-Fit Test Problem for Copulas (2012, unknown): “Among others, see [57], [94], [67], [82], [69].”
- Hierarchical Kendall copulas: Properties and inference (2012, methodology): “This is similar to selection approaches of hierarchical Archimedean copulas Okhrin et al. (2013) and of pair copula constructions Dißmann et al. (2013).”