Approximating multivariate distributions with vines

Alireza Daneshkhah, Tim Bedford

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In a series of papers, Bedford and Cooke used vine (or pair-copulae) as a graphical tool for representing complex high dimensional distributions in terms of bivariate and conditional bivariate distributions or copulae. In this paper, we show that how vines can be used to approximate any given multivariate distribution to any required degree of approximation. This paper is more about the approximation rather than optimal estimation methods. To maintain uniform approximation in the class of copulae used to build the corresponding vine we use minimum information approaches. We generalised the results found by Bedford and Cooke that if a minimal information copula satis¯es each of the (local) constraints (on moments, rank correlation, etc.), then the resulting joint distribution will be also minimally informative given those constraints, to all regular vines. We then apply our results to modelling a dataset of Norwegian financial data that was previously analysed in Aas et al. (2009).
Original languageEnglish
Number of pages29
Publication statusPublished - 14 Sept 2010
EventRoyal Statistical Society - 2010 International Conference - Brighton, United Kingdom
Duration: 13 Sept 201017 Oct 2010


ConferenceRoyal Statistical Society - 2010 International Conference
Country/TerritoryUnited Kingdom


  • multivariate distribution
  • vines
  • bivariate distributions
  • management science
  • approximating


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