Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/31037 
Erscheinungsjahr: 
2006
Schriftenreihe/Nr.: 
Discussion Paper No. 487
Verlag: 
Ludwig-Maximilians-Universität München, Sonderforschungsbereich 386 - Statistische Analyse diskreter Strukturen, München
Zusammenfassung: 
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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