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Risks 2016, 4(4), 33; doi:10.3390/risks4040033

Multivariate TVaR-Based Risk Decomposition for Vector-Valued Portfolios

Department of Mathematics and Statistics, Concordia University, 1400 de Maisonneuve Blvd. West, Montréal, QC H3G 1M8, Canada
Département de Mathématiques et d’Informatique, Université du Québec à Trois-Rivières, 3351, Boulevard des Forges, Trois-Rivières, QC G9A 5H7, Canada
Author to whom correspondence should be addressed.
Academic Editor: Mogens Steffensen
Received: 9 June 2016 / Revised: 6 September 2016 / Accepted: 12 September 2016 / Published: 23 September 2016
View Full-Text   |   Download PDF [388 KB, uploaded 24 October 2016]   |  


In order to protect stakeholders of insurance companies and financial institutions against adverse outcomes of risky businesses, regulators and senior management use capital allocation techniques. For enterprise-wide risk management, it has become important to calculate the contribution of each risk within a portfolio. For that purpose, bivariate lower and upper orthant tail value-at-risk can be used for capital allocation. In this paper, we present multivariate value-at-risk and tail-value-at-risk for d 2 , and we focus on three different methods to calculate optimal values for the contribution of each risk within the sums of random vectors to the overall portfolio, which could particularly apply to insurance and financial portfolios. View Full-Text
Keywords: multivariate tail value-at-risk; risk contribution; capital allocation; risk decomposition multivariate tail value-at-risk; risk contribution; capital allocation; risk decomposition

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Mailhot, M.; Mesfioui, M. Multivariate TVaR-Based Risk Decomposition for Vector-Valued Portfolios. Risks 2016, 4, 33.

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