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A Unified Approach to Systemic Risk Measures via Acceptance Sets

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  • Francesca Biagini
  • Jean-Pierre Fouque
  • Marco Frittelli
  • Thilo Meyer-Brandis

Abstract

The financial crisis has dramatically demonstrated that the traditional approach to apply univariate monetary risk measures to single institutions does not capture sufficiently the perilous systemic risk that is generated by the interconnectedness of the system entities and the corresponding contagion effects. This has brought awareness of the urgent need for novel approaches that capture systemic riskiness. The purpose of this paper is to specify a general methodological framework that is flexible enough to cover a wide range of possibilities to design systemic risk measures via multi-dimensional acceptance sets and aggregation functions, and to study corresponding examples. Existing systemic risk measures can usually be interpreted as the minimal capital needed to secure the system after aggregating individual risks. In contrast, our approach also includes systemic risk measures that can be interpreted as the minimal capital funds that secure the aggregated system by allocating capital to the single institutions before aggregating the individual risks. This allows for a possible ranking of the institutions in terms of systemic riskiness measured by the optimal allocations. Moreover, we also allow for the possibility of allocating the funds according to the future state of the system (random allocation). We provide conditions which ensure monotonicity, convexity, or quasi-convexity properties of our systemic risk measures.

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  • Francesca Biagini & Jean-Pierre Fouque & Marco Frittelli & Thilo Meyer-Brandis, 2015. "A Unified Approach to Systemic Risk Measures via Acceptance Sets," Papers 1503.06354, arXiv.org, revised Apr 2015.
    • Handle: RePEc:arx:papers:1503.06354
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    References listed on IDEAS

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    Cited by:

    1. Yannick Armenti & Stéphane Crépey & Samuel Drapeau & Antonis Papapantoleon, 2018. "Multivariate Shortfall Risk Allocation and Systemic Risk," Working Papers hal-01764398, HAL.
    2. Tobias Fissler & Jana Hlavinov'a & Birgit Rudloff, 2019. "Elicitability and Identifiability of Systemic Risk Measures," Papers 1907.01306, arXiv.org, revised Oct 2019.
    3. Fei Sun & Yichuan Dong, 2020. "Complex risk statistics with scenario analysis," Papers 2003.09255, arXiv.org, revised Nov 2020.
    4. Yu-Sin Chang, 2018. "Systemic Risk and the Dependence Structures," Papers 1809.03425, arXiv.org.
    5. c{C}au{g}{i}n Ararat & Birgit Rudloff, 2016. "Dual representations for systemic risk measures," Papers 1607.03430, arXiv.org, revised Jul 2019.
    6. Fei Sun & Yijun Hu, 2018. "Systemic risk measures with markets volatility," Papers 1812.06185, arXiv.org, revised Jun 2019.
    7. Centrone, Francesca & Rosazza Gianin, Emanuela, 2018. "Capital allocation à la Aumann–Shapley for non-differentiable risk measures," European Journal of Operational Research, Elsevier, vol. 267(2), pages 667-675.
    8. Bikramjit Das & Vicky Fasen, 2016. "Risk contagion under regular variation and asymptotic tail independence," Papers 1603.09406, arXiv.org, revised Apr 2017.
    9. Yannick Armenti & Stephane Crepey & Samuel Drapeau & Antonis Papapantoleon, 2015. "Multivariate Shortfall Risk Allocation and Systemic Risk," Papers 1507.05351, arXiv.org, revised Mar 2017.
    10. Aditya Maheshwari & Andrey Sarantsev, 2017. "Modeling Financial System with Interbank Flows, Borrowing, and Investing," Papers 1707.03542, arXiv.org, revised Oct 2018.
    11. E. Kromer & L. Overbeck & K. Zilch, 2016. "Systemic risk measures on general measurable spaces," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 84(2), pages 323-357, October.
    12. Çağin Ararat & Andreas H. Hamel & Birgit Rudloff, 2017. "Set-Valued Shortfall And Divergence Risk Measures," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 20(05), pages 1-48, August.
    13. Axel Gandy & Luitgard A. M. Veraart, 2017. "A Bayesian Methodology for Systemic Risk Assessment in Financial Networks," Management Science, INFORMS, vol. 63(12), pages 4428-4446, December.

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