Picture of mobile phone running fintech app

Fintech: Open Access research exploring new frontiers in financial technology

Strathprints makes available Open Access scholarly outputs by the Department of Accounting & Finance at Strathclyde. Particular research specialisms include financial risk management and investment strategies.

The Department also hosts the Centre for Financial Regulation and Innovation (CeFRI), demonstrating research expertise in fintech and capital markets. It also aims to provide a strategic link between academia, policy-makers, regulators and other financial industry participants.

Explore all Strathclyde Open Access research...

Approximate uncertainty modeling in risk analysis with vine copulas

Bedford, Tim and Daneshkhah, Alireza and Wilson, Kevin J. (2016) Approximate uncertainty modeling in risk analysis with vine copulas. Risk Analysis, 36 (4). pp. 792-815. ISSN 0272-4332

Text (Bedford-etal-RA-2015-Approximate-uncertainty-modelling-in-risk-analysis)
Final Published Version
License: Creative Commons Attribution 4.0 logo

Download (1MB) | Preview


Many applications of risk analysis require us to jointly model multiple uncertain quantities. Bayesian networks and copulas are two common approaches to modelling joint uncertainties with probability distributions. This paper 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 which do not suffer from some of the restrictions of alternatives such as the multivariate Gaussian copula. The paper 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 which have such good levels of approximation. We extend previous approaches using vines by considering non-constant conditional dependencies which are particularly relevant in financial risk modelling. We discuss how such models may be quantified, in terms of expert judgement or by fitting data, and illustrate the approach by modelling two financial datasets.