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Elicitability and Identifiability of Systemic Risk Measures

Author

Listed:
  • Tobias Fissler
  • Jana Hlavinov'a
  • Birgit Rudloff

Abstract

Identification and scoring functions are statistical tools to assess the calibration and the relative performance of risk measure estimates, e.g., in backtesting. A risk measures is called identifiable (elicitable) it it admits a strict identification function (strictly consistent scoring function). We consider measures of systemic risk introduced in Feinstein, Rudloff and Weber (2017). Since these are set-valued, we work within the theoretical framework of Fissler, Hlavinov\'a and Rudloff (2019) for forecast evaluation of set-valued functionals. We construct oriented selective identification functions, which induce a mixture representation of (strictly) consistent scoring functions. Their applicability is demonstrated with a comprehensive simulation study.

Suggested Citation

  • Tobias Fissler & Jana Hlavinov'a & Birgit Rudloff, 2019. "Elicitability and Identifiability of Systemic Risk Measures," Papers 1907.01306, arXiv.org, revised Oct 2019.
  • Handle: RePEc:arx:papers:1907.01306
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    References listed on IDEAS

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    1. Natalia Nolde & Johanna F. Ziegel, 2016. "Elicitability and backtesting: Perspectives for banking regulation," Papers 1608.05498, arXiv.org, revised Feb 2017.
    2. Hamel, Andreas H. & Kostner, Daniel, 2018. "Cone distribution functions and quantiles for multivariate random variables," Journal of Multivariate Analysis, Elsevier, vol. 167(C), pages 97-113.
    3. 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.
    4. Werner Ehm & Tilmann Gneiting & Alexander Jordan & Fabian Krüger, 2016. "Of quantiles and expectiles: consistent scoring functions, Choquet representations and forecast rankings," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(3), pages 505-562, June.
    5. Tilmann Gneiting & Fadoua Balabdaoui & Adrian E. Raftery, 2007. "Probabilistic forecasts, calibration and sharpness," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(2), pages 243-268, April.
    6. Patton, Andrew J., 2011. "Data-based ranking of realised volatility estimators," Journal of Econometrics, Elsevier, vol. 161(2), pages 284-303, April.
    7. Gneiting, Tilmann & Raftery, Adrian E., 2007. "Strictly Proper Scoring Rules, Prediction, and Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 359-378, March.
    8. Elyès Jouini & Walter Schachermayer & Nizar Touzi, 2006. "Law Invariant Risk Measures Have the Fatou Property," Post-Print halshs-00176522, HAL.
    9. repec:dau:papers:123456789/342 is not listed on IDEAS
    10. Hans Föllmer & Stefan Weber, 2015. "The Axiomatic Approach to Risk Measures for Capital Determination," Annual Review of Financial Economics, Annual Reviews, vol. 7(1), pages 301-337, December.
    11. Tsyplakov, Alexander, 2014. "Theoretical guidelines for a partially informed forecast examiner," MPRA Paper 55017, University Library of Munich, Germany.
    12. repec:cup:judgdm:v:10:y:2015:i:5:p:456-468 is not listed on IDEAS
    13. C. Heinrich, 2014. "The mode functional is not elicitable," Biometrika, Biometrika Trust, vol. 101(1), pages 245-251.
    14. Hoffmann, Hannes & Meyer-Brandis, Thilo & Svindland, Gregor, 2016. "Risk-consistent conditional systemic risk measures," Stochastic Processes and their Applications, Elsevier, vol. 126(7), pages 2014-2037.
    15. Francesca Biagini & Jean‐Pierre Fouque & Marco Frittelli & Thilo Meyer‐Brandis, 2019. "A unified approach to systemic risk measures via acceptance sets," Mathematical Finance, Wiley Blackwell, vol. 29(1), pages 329-367, January.
    16. Newey, Whitney K & Powell, James L, 1987. "Asymmetric Least Squares Estimation and Testing," Econometrica, Econometric Society, vol. 55(4), pages 819-847, July.
    17. Hannes Hoffmann & Thilo Meyer-Brandis & Gregor Svindland, 2016. "Risk-Consistent Conditional Systemic Risk Measures," Papers 1609.07897, arXiv.org.
    18. Hajo Holzmann & Matthias Eulert, 2014. "The role of the information set for forecasting - with applications to risk management," Papers 1404.7653, arXiv.org.
    19. David Bolin & Finn Lindgren, 2015. "Excursion and contour uncertainty regions for latent Gaussian models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 77(1), pages 85-106, January.
    20. Valeria Bignozzi & Matteo Burzoni & Cosimo Munari, 2018. "Risk Measures Based on Benchmark Loss Distributions," Swiss Finance Institute Research Paper Series 18-48, Swiss Finance Institute, revised Nov 2018.
    21. Hans Föllmer & Alexander Schied, 2002. "Convex measures of risk and trading constraints," Finance and Stochastics, Springer, vol. 6(4), pages 429-447.
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    Cited by:

    1. Ruodu Wang & Yunran Wei, 2020. "Risk functionals with convex level sets," Mathematical Finance, Wiley Blackwell, vol. 30(4), pages 1337-1367, October.
    2. Tobias Fissler & Jana Hlavinová & Birgit Rudloff, 2021. "Elicitability and identifiability of set-valued measures of systemic risk," Finance and Stochastics, Springer, vol. 25(1), pages 133-165, January.

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