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Data and uncertainty in extreme risks - a nonlinear expectations approach

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  • Samuel N. Cohen

Abstract

Estimation of tail quantities, such as expected shortfall or Value at Risk, is a difficult problem. We show how the theory of nonlinear expectations, in particular the Data-robust expectation introduced in [5], can assist in the quantification of statistical uncertainty for these problems. However, when we are in a heavy-tailed context (in particular when our data are described by a Pareto distribution, as is common in much of extreme value theory), the theory of [5] is insufficient, and requires an additional regularization step which we introduce. By asking whether this regularization is possible, we obtain a qualitative requirement for reliable estimation of tail quantities and risk measures, in a Pareto setting.

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  • Samuel N. Cohen, 2017. "Data and uncertainty in extreme risks - a nonlinear expectations approach," Papers 1705.08301, arXiv.org, revised Feb 2018.
    • Handle: RePEc:arx:papers:1705.08301
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    References listed on IDEAS

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    1. Frittelli, Marco & Rosazza Gianin, Emanuela, 2002. "Putting order in risk measures," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1473-1486, July.
    2. Philippe Artzner & Freddy Delbaen & Jean‐Marc Eber & David Heath, 1999. "Coherent Measures of Risk," Mathematical Finance, Wiley Blackwell, vol. 9(3), pages 203-228, July.
    3. Barrieu, Pauline & El Karoui, Nicole, 2005. "Inf-convolution of risk measures and optimal risk transfer," LSE Research Online Documents on Economics 2829, London School of Economics and Political Science, LSE Library.
    4. Pauline Barrieu & Nicole El Karoui, 2005. "Inf-convolution of risk measures and optimal risk transfer," Finance and Stochastics, Springer, vol. 9(2), pages 269-298, April.
    5. Alexander Cherny & Dilip Madan, 2009. "New Measures for Performance Evaluation," The Review of Financial Studies, Society for Financial Studies, vol. 22(7), pages 2371-2406, July.
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    Cited by:

    1. Patryk Gierjatowicz & Marc Sabate-Vidales & David v{S}iv{s}ka & Lukasz Szpruch & v{Z}an v{Z}uriv{c}, 2020. "Robust pricing and hedging via neural SDEs," Papers 2007.04154, arXiv.org.
    2. Samuel N. Cohen & Tanut Treetanthiploet, 2019. "Gittins' theorem under uncertainty," Papers 1907.05689, arXiv.org, revised Jun 2021.

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