New Prospects on Vines
In this paper, we present a new methodology based on vine copulas to estimate multivariate distributions in high dimensions, taking advantage of the diversity of vine copulas. Considering the huge number of vine copulas in dimension n, we introduce an efficient selection algorithm to build and select vine copulas with respect to any test T. Our methodology offers a great flexibility to practitioners to compute VaR associated to a portfolio in high dimension.
|Date of creation:||Mar 2010|
|Publication status:||Published in Documents de travail du Centre d'Economie de la Sorbonne 2008.95 - ISSN : 1955-611X. 2010|
|Note:||View the original document on HAL open archive server: https://halshs.archives-ouvertes.fr/halshs-00348884v3|
|Contact details of provider:|| Web page: https://hal.archives-ouvertes.fr/|
When requesting a correction, please mention this item's handle: RePEc:hal:cesptp:halshs-00348884. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (CCSD)
If references are entirely missing, you can add them using this form.