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A Breakthrough Idea in Risk Measure Validation – Is the Way Paved for an Effective Expected Shortfall Backtest?

Author

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  • Gyöngyi Bugár

    (University of Pécs)

Abstract

This research note is a kind of “call for attention” to recent developments in backtesting financial risk measures. This topic is relevant in relation to the regulatory monitoring of the performance of internal risk models used by banks in determining the minimum capital requirements for trading book portfolios. Backtesting is a process for checking the validity of risk estimation models. In his seminal work, Gneiting (2011) has proven that a prominent risk measure, Expected Shortfall (ES), lacks a property called elicitability. This finding has triggered a huge controversy on the issue of whether ES is backtestable at all. Due to the significant contribution of Acerbi and Székely (2017, 2019) among others, the above-mentioned debate can be adequately and convincingly closed because there is a (re)solution. In particular, one can arrive at the conclusion that, building on its joint elicitability with Value-at- Risk (VaR), it is possible to introduce a so-called ridge backtest for ES. In fact, there is still an open question as to when and how the regulatory authorities will (re)act.

Suggested Citation

  • Gyöngyi Bugár, 2019. "A Breakthrough Idea in Risk Measure Validation – Is the Way Paved for an Effective Expected Shortfall Backtest?," Financial and Economic Review, Magyar Nemzeti Bank (Central Bank of Hungary), vol. 18(4), pages 130-145.
    • Handle: RePEc:mnb:finrev:v:18:y:2019:i:4:p:130-145
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    References listed on IDEAS

    as
    1. Gneiting, Tilmann, 2011. "Making and Evaluating Point Forecasts," Journal of the American Statistical Association, American Statistical Association, vol. 106(494), pages 746-762.
    2. Johanna F. Ziegel, 2016. "Coherence And Elicitability," Mathematical Finance, Wiley Blackwell, vol. 26(4), pages 901-918, October.
    3. Fabio Bellini & Valeria Bignozzi, 2015. "On elicitable risk measures," Quantitative Finance, Taylor & Francis Journals, vol. 15(5), pages 725-733, May.
    4. Acerbi, Carlo & Tasche, Dirk, 2002. "On the coherence of expected shortfall," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1487-1503, July.
    5. Alexander J. McNeil & Rüdiger Frey & Paul Embrechts, 2015. "Quantitative Risk Management: Concepts, Techniques and Tools Revised edition," Economics Books, Princeton University Press, edition 2, number 10496.
    6. Gyöngyi Bugár & Anita Ratting, 2016. "Revision of the quantification of market risk in the Basel III regulatory framework," Financial and Economic Review, Magyar Nemzeti Bank (Central Bank of Hungary), vol. 15(1), pages 33-50.
    7. Philippe Artzner & Freddy Delbaen & Jean‐Marc Eber & David Heath, 1999. "Coherent Measures of Risk," Mathematical Finance, Wiley Blackwell, vol. 9(3), pages 203-228, July.
    8. Rockafellar, R. Tyrrell & Uryasev, Stanislav, 2002. "Conditional value-at-risk for general loss distributions," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1443-1471, July.
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    More about this item

    Keywords

    banking regulation; ES; elicitability; backtestability; ridge backtest;
    All these keywords.

    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation

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