Approximate uncertainty modeling in risk analysis with vine copulas
| dc.contributor.author | Bedford, Tim | |
| dc.contributor.author | Daneshkhah, Alireza | |
| dc.contributor.author | Wilson, Kevin J. | |
| dc.date.accessioned | 2016-03-09T14:58:32Z | |
| dc.date.available | 2016-03-09T14:58:32Z | |
| dc.date.issued | 2015-09-02 | |
| dc.description.abstract | 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. | en_UK |
| dc.identifier.citation | Bedford, T., Daneshkhah, A. and Wilson, K. J. (2015), Approximate Uncertainty Modeling in Risk Analysis with Vine Copulas. Risk Analysis, Vol. 36, Iss. 4, pp. 792-815 | en_UK |
| dc.identifier.issn | 0272-4332 | |
| dc.identifier.uri | http://dx.doi.org/10.1111/risa.12471 | |
| dc.identifier.uri | http://dspace.lib.cranfield.ac.uk/handle/1826/9769 | |
| dc.language.iso | en | en_UK |
| dc.publisher | Wiley | en_UK |
| dc.rights | Attribution 4.0 International (CC BY 4.0) You are free to: Share — copy and redistribute the material in any medium or format, Adapt — remix, transform, and build upon the material for any purpose, even commercially. The licensor cannot revoke these freedoms as long as you follow the license terms. Under the following terms: Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. Information: No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits. | |
| dc.subject | Copula | en_UK |
| dc.subject | entropy | en_UK |
| dc.subject | information | en_UK |
| dc.subject | risk modeling | en_UK |
| dc.subject | vine | en_UK |
| dc.title | Approximate uncertainty modeling in risk analysis with vine copulas | en_UK |
| dc.type | Article | en_UK |