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An Econometric Study of Vine Copulas

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Abstract

We present a new recursive algorithm to construct vine copulas based on an underlying tree structure. This new structure is interesting to compute multivariate distributions for dependent random variables. We proove the asymptotic normality of the vine copula parameter estimator and show that all vine copula parameter estimators have comparable variance. Both results are crucial to motivate any econometrical work based on vine copulas. We provide an application of vine copulas to estimate the VaR of a portfolio, and show they offer significant improvement as compared to a benchmark estimator based on a GARCH model.

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File URL: ftp://mse.univ-paris1.fr/pub/mse/CES2010/10040.pdf
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Bibliographic Info

Paper provided by Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne in its series Documents de travail du Centre d'Economie de la Sorbonne with number 10040.

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Length: 19 pages
Date of creation: May 2010
Date of revision:
Handle: RePEc:mse:cesdoc:10040

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Keywords: Vines copulas; conditional copulas; risk management.;

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Cited by:
  1. Dominique Guegan & Bertrand Hassani, 2012. "Multivariate VaRs for Operational Risk Capital Computation : a Vine Structure Approach," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00587706, HAL.
  2. Dominique Guegan & Bertrand Hassani, 2011. "Multivariate VaRs for Operational Risk Capital Computation : a Vine Structure Approach," Documents de travail du Centre d'Economie de la Sorbonne 11017, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.

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