Truncated regular vines in high dimensions with application to financial data

E. C. Brechmann, C. Czado, K. Aas

Research output: Contribution to journalArticlepeer-review

225 Scopus citations

Abstract

Using only bivariate copulas as building blocks, regular vine copulas constitute a flexible class of high-dimensional dependency models. However, the flexibility comes along with an exponentially increasing complexity in larger dimensions. In order to counteract this problem, we propose using statistical model selection techniques to either truncate or simplify a regular vine copula. As a special case, we consider the simplification of a canonical vine copula using a multivariate copula as previously treated by Heinen & Valdesogo (2009) and Valdesogo (2009). We validate the proposed approaches by extensive simulation studies and use them to investigate a 19-dimensional financial data set of Norwegian and international market variables.

Original languageEnglish
Pages (from-to)68-85
Number of pages18
JournalCanadian Journal of Statistics
Volume40
Issue number1
DOIs
StatePublished - Mar 2012

Keywords

  • Multivariate copula
  • Regular vines
  • Simplified vines
  • Truncated canonical vines

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