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What Is the Best Risk Measure in Practice? A Comparison of Standard Measures

Author

Listed:
  • Suzanne Emmer

    (CREAR - Center of Research in Econo-finance and Actuarial sciences on Risk / Centre de Recherche Econo-financière et Actuarielle sur le Risque - ESSEC Business School)

  • Marie Kratz

    (ESSEC Business School, MAP5 - UMR 8145 - Mathématiques Appliquées Paris 5 - UPD5 - Université Paris Descartes - Paris 5 - INSMI-CNRS - Institut National des Sciences Mathématiques et de leurs Interactions - CNRS Mathématiques - CNRS - Centre National de la Recherche Scientifique)

  • Dirk Tasche

    (Prudential Regulation Authority - Bank of England)

Abstract

Expected Shortfall (ES) has been widely accepted as a risk measure that is conceptually superior to Value-at-Risk (VaR). At the same time, however, it has been criticized for issues relating to backtesting. In particular, ES has been found not to be elicitable which means that backtesting for ES is less straight-forward than, e.g., backtesting for VaR. Expectiles have been suggested as potentially better alternatives to both ES and VaR. In this paper, we revisit commonly accepted desirable properties of risk measures like coherence, comonotonic additivity, robustness and elicitability. We check VaR, ES and Expectiles with regard to whether or not they enjoy these properties, with particular emphasis on Expectiles. We also consider their impact on capital allocation, an important issue in risk management. We find that, despite the caveats that apply to the estimation and backtesting of ES, it can be considered a good risk measure. In particular, there is no sufficient evidence to justify an all-inclusive replacement of ES by Expectiles in applications, especially as we provide an alternative way for backtesting of ES.

Suggested Citation

  • Suzanne Emmer & Marie Kratz & Dirk Tasche, 2013. "What Is the Best Risk Measure in Practice? A Comparison of Standard Measures," Working Papers hal-00921283, HAL.
    • Handle: RePEc:hal:wpaper:hal-00921283
    Note: View the original document on HAL open archive server: https://essec.hal.science/hal-00921283
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    References listed on IDEAS

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    2. Rafael Frongillo & Ian A. Kash, 2015. "Elicitation Complexity of Statistical Properties," Papers 1506.07212, arXiv.org, revised Aug 2020.
    3. Panna Miskolczi, 2016. "Differences Between Mean-Variance And Mean-Cvar Portfolio Optimization Models," Annals of Faculty of Economics, University of Oradea, Faculty of Economics, vol. 1(1), pages 548-557, July.
    4. Marc Busse & Michel Dacorogna & Marie Kratz, 2014. "The Impact of Systemic Risk on the Diversification Benefits of a Risk Portfolio," Risks, MDPI, vol. 2(3), pages 1-17, July.
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    6. Fabio Bellini & Valeria Bignozzi, 2015. "On elicitable risk measures," Quantitative Finance, Taylor & Francis Journals, vol. 15(5), pages 725-733, May.
    7. Ruodu Wang & Johanna F. Ziegel, 2014. "Distortion Risk Measures and Elicitability," Papers 1405.3769, arXiv.org, revised May 2014.
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    9. Michele Leonardo Bianchi & Gian Luca Tassinari & Frank J. Fabozzi, 2016. "Riding With The Four Horsemen And The Multivariate Normal Tempered Stable Model," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 19(04), pages 1-28, June.
    10. Kellner, Ralf & Rösch, Daniel, 2016. "Quantifying market risk with Value-at-Risk or Expected Shortfall? – Consequences for capital requirements and model risk," Journal of Economic Dynamics and Control, Elsevier, vol. 68(C), pages 45-63.

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