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.

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Year of publication:	2008-12
Authors:	Guegan, Dominique ; Maugis, Pierre-André
Institutions:	Centre d'Économie de la Sorbonne, Université Paris 1 (Panthéon-Sorbonne)
Subject:	Vine copulas \| multivariate copulas \| model selection \| VaR

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Extent:	application/pdf
Series:	Documents de travail du Centre d'Economie de la Sorbonne. - ISSN 1955-611X.
Type of publication:	Book / Working Paper
Notes:	14 pages
Classification:	D81 - Criteria for Decision-Making under Risk and Uncertainty ; C10 - Econometric and Statistical Methods: General. General ; C40 - Econometric and Statistical Methods: Special Topics. General ; C52 - Model Evaluation and Testing
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10004988963