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Should the advanced measurement approach be replaced with the standardized measurement approach for operational risk?


  • Gareth W. Peters
  • Pavel V. Shevchenko
  • Bertrand Hassani
  • Ariane Chapelle


Recently, Basel Committee for Banking Supervision proposed to replace all approaches, including Advanced Measurement Approach (AMA), for operational risk capital with a simple formula referred to as the Standardised Measurement Approach (SMA). This paper discusses and studies the weaknesses and pitfalls of SMA such as instability, risk insensitivity, super-additivity and the implicit relationship between SMA capital model and systemic risk in the banking sector. We also discuss the issues with closely related operational risk Capital-at-Risk (OpCar) Basel Committee proposed model which is the precursor to the SMA. In conclusion, we advocate to maintain the AMA internal model framework and suggest as an alternative a number of standardization recommendations that could be considered to unify internal modelling of operational risk. The findings and views presented in this paper have been discussed with and supported by many OpRisk practitioners and academics in Australia, Europe, UK and USA, and recently at OpRisk Europe 2016 conference in London.

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  • Gareth W. Peters & Pavel V. Shevchenko & Bertrand Hassani & Ariane Chapelle, 2016. "Should the advanced measurement approach be replaced with the standardized measurement approach for operational risk?," Papers 1607.02319,, revised Sep 2016.
  • Handle: RePEc:arx:papers:1607.02319

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    References listed on IDEAS

    1. Gareth W. Peters & Wilson Y. Chen & Richard H. Gerlach, 2016. "Estimating Quantile Families of Loss Distributions for Non-Life Insurance Modelling via L-moments," Papers 1603.01041,
    2. Dong, Alice X.D. & Chan, Jennifer S.K. & Peters, Gareth W., 2015. "Risk Margin Quantile Function via Parametric and Non-Parametric Bayesian Approaches," ASTIN Bulletin: The Journal of the International Actuarial Association, Cambridge University Press, vol. 45(03), pages 503-550, September.
    3. Gareth W. Peters & Rodrigo S. Targino & Pavel V. Shevchenko, 2013. "Understanding Operational Risk Capital Approximations: First and Second Orders," Papers 1303.2910,
    4. Gareth W. Peters & Wilson Ye Chen & Richard H. Gerlach, 2016. "Estimating Quantile Families of Loss Distributions for Non-Life Insurance Modelling via L-Moments," Risks, MDPI, Open Access Journal, vol. 4(2), pages 1-41, May.
    5. Gareth W. Peters & Pavel V. Shevchenko & Mario V. Wuthrich, 2009. "Dynamic operational risk: modeling dependence and combining different sources of information," Papers 0904.4074,, revised Jul 2009.
    6. repec:gam:jrisks:v:4:y:2016:i:2:p:14:d:70470 is not listed on IDEAS
    7. Stasinopoulos, D. Mikis & Rigby, Robert A., 2007. "Generalized Additive Models for Location Scale and Shape (GAMLSS) in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 23(i07).
    8. Ganegoda, Amandha & Evans, John, 2013. "A scaling model for severity of operational losses using generalized additive models for location scale and shape (GAMLSS)," Annals of Actuarial Science, Cambridge University Press, vol. 7(01), pages 61-100, March.
    9. Kabir Dutta & Jason Perry, 2006. "A tale of tails: an empirical analysis of loss distribution models for estimating operational risk capital," Working Papers 06-13, Federal Reserve Bank of Boston.
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    Cited by:

    1. repec:eee:phsmap:v:516:y:2019:i:c:p:327-339 is not listed on IDEAS
    2. Migueis, Marco, 2017. "Forward-looking and Incentive-compatible Operational Risk Capital Framework," Finance and Economics Discussion Series 2017-087, Board of Governors of the Federal Reserve System (US), revised 07 Aug 2018.

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