Pair-copula constructions of multiple dependence
Building on the work of Bedford, Cooke and Joe, we show how multivariate data, which exhibit complex patterns of dependence in the tails, can be modelled using a cascade of pair-copulae, acting on two variables at a time. We use the pair-copula decomposition of a general multivariate distribution and propose a method to perform inference. The model construction is hierarchical in nature, the various levels corresponding as simple building blocs. Pair-copula decomposed models also represent a very flexible way to construct higher-dimensional coplulae. We apply the methodology to a financial data set. Our approach represents the first step towards developing of an unsupervised algorithm that explores the space of possible pair-copula models, that also can be applied to huge data sets automatically.
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|Authors:||Aas, Kjersti ; Czado, Claudia ; Frigessi, Arnoldo ; Bakken, Henrik|
München : Techn. Univ.; Sonderforschungsbereich 386, Statistische Analyse Diskreter Strukturen
|Type of publication:||Book / Working Paper|
|Type of publication (narrower categories):||Working Paper|
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