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More accurate measurement for enhanced controls: VaR vs ES?

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  • Guegan, Dominique
  • Hassani, Bertrand K.

Abstract

This paper (this work was achieved through the Laboratory of Excellence on Financial Regulation (Labex ReFi) supported by PRES heSam under the reference ANR-10-LABEX-0095) analyses how risks are measured in financial institutions, for instance Market, Credit, Operational, among others with respect to the choice of risk measures, the choice of distributions used to model them and the level of confidence selected. We discuss and illustrate the characteristics, the paradoxes and the issues observed, comparing the Value-at-Risk and the Expected Shortfall in practice. This paper is built as a differential diagnosis and aims at discussing the reliability of the risk measures and making some recommendations. (This paper has been written in a very particular period of time as most regulatory papers written in the past 20years are currently being questioned by both practitioners and regulators themselves. Some disarray has been observed among risk managers as most models required by the regulation have not been consistent with their own objective of risk management. The enlightenment brought by this paper is based on an academic analysis of the issues engendered by some pieces of regulation and its purpose is not to create any sort of controversy.)

Suggested Citation

  • Guegan, Dominique & Hassani, Bertrand K., 2018. "More accurate measurement for enhanced controls: VaR vs ES?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 54(C), pages 152-165.
    • Handle: RePEc:eee:intfin:v:54:y:2018:i:c:p:152-165
    DOI: 10.1016/j.intfin.2017.06.002
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    Citations

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    Cited by:

    1. George Tzagkarakis & Frantz Maurer, 2020. "An energy-based measure for long-run horizon risk quantification," Annals of Operations Research, Springer, vol. 289(2), pages 363-390, June.
    2. Krzysztof Echaust & Małgorzata Just, 2020. "Value at Risk Estimation Using the GARCH-EVT Approach with Optimal Tail Selection," Mathematics, MDPI, vol. 8(1), pages 1-24, January.
    3. Santiago Carrillo Menéndez & Bertrand Kian Hassani, 2021. "Expected Shortfall Reliability—Added Value of Traditional Statistics and Advanced Artificial Intelligence for Market Risk Measurement Purposes," Mathematics, MDPI, vol. 9(17), pages 1-20, September.
    4. Dominique Guegan & Bertrand K. Hassani, 2019. "Risk Measurement," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-02119256, HAL.
    5. Inés Jiménez & Andrés Mora-Valencia & Trino-Manuel Ñíguez & Javier Perote, 2020. "Portfolio Risk Assessment under Dynamic (Equi)Correlation and Semi-Nonparametric Estimation: An Application to Cryptocurrencies," Mathematics, MDPI, vol. 8(12), pages 1-24, November.
    6. Osmundsen, Kjartan Kloster, 2018. "Using expected shortfall for credit risk regulation," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 57(C), pages 80-93.
    7. Hamed Tabasi & Vahidreza Yousefi & Jolanta Tamošaitienė & Foroogh Ghasemi, 2019. "Estimating Conditional Value at Risk in the Tehran Stock Exchange Based on the Extreme Value Theory Using GARCH Models," Administrative Sciences, MDPI, vol. 9(2), pages 1-17, May.

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