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Multivariate TVaR-Based Risk Decomposition for Vector-Valued Portfolios

Author

Listed:
  • Mélina Mailhot

    (Department of Mathematics and Statistics, Concordia University, 1400 de Maisonneuve Blvd. West, Montréal, QC H3G 1M8, Canada)

  • Mhamed Mesfioui

    (Département de Mathématiques et d’Informatique, Université du Québec à Trois-Rivières, 3351, Boulevard des Forges, Trois-Rivières, QC G9A 5H7, Canada)

Abstract

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.

Suggested Citation

  • Mélina Mailhot & Mhamed Mesfioui, 2016. "Multivariate TVaR-Based Risk Decomposition for Vector-Valued Portfolios," Risks, MDPI, vol. 4(4), pages 1-16, September.
    • Handle: RePEc:gam:jrisks:v:4:y:2016:i:4:p:33-:d:78760
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    References listed on IDEAS

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    Cited by:

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    2. Jaunė, Eglė & Šiaulys, Jonas, 2022. "Asymptotic risk decomposition for regularly varying distributions with tail dependence," Applied Mathematics and Computation, Elsevier, vol. 427(C).

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