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Lookup NU author(s): Dr Kevin Wilson
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Many applications of risk analysis require us to jointly model multiple uncertain quantities. Bayesian networks and copulas are two common approaches to modeling joint uncertainties with probability distributions. This article focuses on new methodologies for copulas by developing work of Cooke, Bedford, Kurowica, and others on vines as a way of constructing higher dimensional distributions that do not suffer from some of the restrictions of alternatives such as the multivariate Gaussian copula. The article provides a fundamental approximation result, demonstrating that we can approximate any density as closely as we like using vines. It further operationalizes this result by showing how minimum information copulas can be used to provide parametric classes of copulas that have such good levels of approximation. We extend previous approaches using vines by considering nonconstant conditional dependencies, which are particularly relevant in financial risk modeling. We discuss how such models may be quantified, in terms of expert judgment or by fitting data, and illustrate the approach by modeling two financial data sets.
Author(s): Bedford T, Daneshkhah A, Wilson KJ
Publication type: Article
Publication status: Published
Journal: Risk Analysis
Print publication date: 01/04/2016
Online publication date: 02/09/2015
Acceptance date: 15/07/2015
Date deposited: 15/09/2015
ISSN (print): 0272-4332
ISSN (electronic): 1539-6924
Publisher: Wiley-Blackwell Publishing, Inc.
Notes: Article is Gold Open Access
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