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Multivariate Geometric Tail- And Range-Value-At-Risk

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  • Herrmann, Klaus
  • Hofert, Marius
  • Mailhot, Mélina

Abstract

A generalization of range-value-at-risk (RVaR) and tail-value-at-risk (TVaR) for d-dimensional distribution functions is introduced. Properties of these new risk measures are studied and illustrated. We provide special cases, applications and a comparison with traditional univariate and multivariate versions of the TVaR and RVaR.

Suggested Citation

  • Herrmann, Klaus & Hofert, Marius & Mailhot, Mélina, 2020. "Multivariate Geometric Tail- And Range-Value-At-Risk," ASTIN Bulletin, Cambridge University Press, vol. 50(1), pages 265-292, January.
    • Handle: RePEc:cup:astinb:v:50:y:2020:i:1:p:265-292_9
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    Cited by:

    1. Shuo Gong & Yijun Hu & Linxiao Wei, 2022. "Risk measurement of joint risk of portfolios: a liquidity shortfall aspect," Papers 2212.04848, arXiv.org.
    2. Beck, Nicholas & Di Bernardino, Elena & Mailhot, Mélina, 2021. "Semi-parametric estimation of multivariate extreme expectiles," Journal of Multivariate Analysis, Elsevier, vol. 184(C).
    3. Baishuai Zuo & Chuancun Yin & Jing Yao, 2023. "Multivariate range Value-at-Risk and covariance risk measures for elliptical and log-elliptical distributions," Papers 2305.09097, arXiv.org.

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