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Risk Measure Inference

Author

Listed:
  • Christophe Hurlin

    (LEO - Laboratoire d'économie d'Orleans [2008-2011] - UO - Université d'Orléans - CNRS - Centre National de la Recherche Scientifique)

  • Sébastien Laurent

    (LKB (Lhomond) - Laboratoire Kastler Brossel - FRDPENS - Fédération de recherche du Département de physique de l'Ecole Normale Supérieure - ENS Paris - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique - UPMC - Université Pierre et Marie Curie - Paris 6 - CNRS - Centre National de la Recherche Scientifique, GREQAM - Groupement de Recherche en Économie Quantitative d'Aix-Marseille - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique)

  • Rogier Quaedvlieg

    (Maastricht University [Maastricht])

  • Stephan Smeekes

    (Maastricht University [Maastricht])

Abstract

We propose a bootstrap-based test of the null hypothesis of equality of two firms? conditional Risk Measures (RMs) at a single point in time. The test can be applied to a wide class of conditional risk measures issued from parametric or semi-parametric models. Our iterative testing procedure produces a grouped ranking of the RMs, which has direct application for systemic risk analysis. Firms within a group are statistically indistinguishable form each other, but significantly more risky than the firms belonging to lower ranked groups. A Monte Carlo simulation demonstrates that our test has good size and power properties. We apply the procedure to a sample of 94 U.S. financial institutions using ?CoVaR, MES, and %SRISK. We find that for some periods and RMs, we cannot statistically distinguish the 40 most risky firms due to estimation uncertainty.

Suggested Citation

  • Christophe Hurlin & Sébastien Laurent & Rogier Quaedvlieg & Stephan Smeekes, 2017. "Risk Measure Inference," Post-Print hal-01457393, HAL.
    • Handle: RePEc:hal:journl:hal-01457393
    DOI: 10.1080/07350015.2015.1127815
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    Cited by:

    1. Javier Ojea Ferreiro, 2025. "A Market-Based Approach to Reverse Stress Testing the Financial System," Staff Working Papers 25-32, Bank of Canada.
    2. Francq, Christian & Zakoian, Jean-Michel, 2015. "Looking for efficient qml estimation of conditional value-at-risk at multiple risk levels," MPRA Paper 67195, University Library of Munich, Germany.
    3. Francq, Christian & Zakoïan, Jean-Michel, 2020. "Virtual Historical Simulation for estimating the conditional VaR of large portfolios," Journal of Econometrics, Elsevier, vol. 217(2), pages 356-380.
    4. Hassanniakalager, Arman & Baker, Paul L. & Platanakis, Emmanouil, 2024. "A False Discovery Rate approach to optimal volatility forecasting model selection," International Journal of Forecasting, Elsevier, vol. 40(3), pages 881-902.
    5. Sarlin, Peter & Holopainen, Markus, 2016. "Toward robust early-warning models: a horse race, ensembles and model uncertainty," Working Paper Series 1900, European Central Bank.
    6. Ophélie Couperier & Jérémy Leymarie, 2020. "Backtesting Expected Shortfall via Multi-Quantile Regression," Working Papers halshs-01909375, HAL.
    7. Hurlin, Christophe & Leymarie, Jérémy & Patin, Antoine, 2018. "Loss functions for Loss Given Default model comparison," European Journal of Operational Research, Elsevier, vol. 268(1), pages 348-360.
    8. Raphaëlle BELLANDO & Oana TOADER, 2017. "An analysis of banks’ weaknesses in the light of stress tests," LEO Working Papers / DR LEO 2479, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    9. Gordy, Michael B. & McNeil, Alexander J., 2020. "Spectral backtests of forecast distributions with application to risk management," Journal of Banking & Finance, Elsevier, vol. 116(C).
    10. Markus Holopainen & Peter Sarlin, 2015. "Toward robust early-warning models: A horse race, ensembles and model uncertainty," Papers 1501.04682, arXiv.org, revised Apr 2016.
    11. Wided Khiari & Salim Ben Sassi, 2019. "On Identifying the Systemically Important Tunisian Banks: An Empirical Approach Based on the △CoVaR Measures," Risks, MDPI, vol. 7(4), pages 1-15, December.
    12. Saidane, Dhafer & Sène, Babacar & Désiré Kanga, Kouamé, 2021. "Pan-African banks, banking interconnectivity: A new systemic risk measure in the WAEMU," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 74(C).

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