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Bregman superquantiles. Estimation methods and applications

Author

Listed:
  • Tatiana Labopin-Richard

    (IMT)

  • Fabrice Gamboa

    (IMT)

  • Aur'elien Garivier

    (IMT)

  • Bertrand Iooss

    (GdR MASCOT-NUM)

Abstract

In this work, we extend some quantities introduced in "Optimization of conditional value-at-risk" of R.T Rockafellar and S. Uryasev to the case where the proximity between real numbers is measured by using a Bregman divergence. This leads to the definition of the Bregman superquantile. Axioms of a coherent measure of risk discussed in "Coherent approches to risk in optimization under uncertainty" of R.T Rockafellar are studied in the case of Bregman superquantile. Furthermore, we deal with asymptotic properties of a Monte Carlo estimator of the Bregman superquantile.

Suggested Citation

  • Tatiana Labopin-Richard & Fabrice Gamboa & Aur'elien Garivier & Bertrand Iooss, 2014. "Bregman superquantiles. Estimation methods and applications," Papers 1405.6677, arXiv.org, revised Jan 2016.
    • Handle: RePEc:arx:papers:1405.6677
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    References listed on IDEAS

    as
    1. Len Umantsev & Victor Chernozhukov, 2001. "Conditional value-at-risk: Aspects of modeling and estimation," Empirical Economics, Springer, vol. 26(1), pages 271-292.
    2. Acerbi, Carlo & Tasche, Dirk, 2002. "On the coherence of expected shortfall," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1487-1503, July.
    3. So Yeon Chun & Alexander Shapiro & Stan Uryasev, 2012. "Conditional Value-at-Risk and Average Value-at-Risk: Estimation and Asymptotics," Operations Research, INFORMS, vol. 60(4), pages 739-756, August.
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    Cited by:

    1. Gadat, Sébastien & Costa, Manon, 2020. "Non asymptotic controls on a stochastic algorithm for superquantile approximation," TSE Working Papers 20-1149, Toulouse School of Economics (TSE).
    2. Manon Costa & Sébastien Gadat, 2021. "Non-asymptotic study of a recursive superquantile estimation algorithm," Post-Print hal-03610477, HAL.

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