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

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

Abstract

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.

Suggested Citation

  • 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, arXiv.org, revised Sep 2016.
    • Handle: RePEc:arx:papers:1607.02319
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    References listed on IDEAS

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    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, arXiv.org.
    2. Gareth W. Peters & Rodrigo S. Targino & Pavel V. Shevchenko, 2013. "Understanding Operational Risk Capital Approximations: First and Second Orders," Papers 1303.2910, arXiv.org.
    3. 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, Cambridge University Press, vol. 45(3), pages 503-550, September.
    4. 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).
    5. 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(1), pages 61-100, March.
    6. 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, vol. 4(2), pages 1-41, May.
    7. Gareth W. Peters & Pavel V. Shevchenko & Mario V. Wuthrich, 2009. "Dynamic operational risk: modeling dependence and combining different sources of information," Papers 0904.4074, arXiv.org, revised Jul 2009.
    8. 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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    Citations

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    Cited by:

    1. Marco Migueis, 2019. "Evaluating the AMA and the new standardized approach for operational risk capital," Journal of Banking Regulation, Palgrave Macmillan, vol. 20(4), pages 302-311, December.
    2. Marco Migueis, 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 (U.S.).
    3. Lu Wei & Jianping Li & Xiaoqian Zhu, 2018. "Operational Loss Data Collection: A Literature Review," Annals of Data Science, Springer, vol. 5(3), pages 313-337, September.
    4. Martin Eling & Kwangmin Jung, 2022. "Heterogeneity in cyber loss severity and its impact on cyber risk measurement," Risk Management, Palgrave Macmillan, vol. 24(4), pages 273-297, December.
    5. Xu, Chi & Zheng, Chunling & Wang, Donghua & Ji, Jingru & Wang, Nuan, 2019. "Double correlation model for operational risk: Evidence from Chinese commercial banks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 327-339.
    6. Xiaoqian Zhu & Jianping Li & Dengsheng Wu, 2019. "Should the Advanced Measurement Approach for Operational Risk be Discarded? Evidence from the Chinese Banking Industry," Review of Pacific Basin Financial Markets and Policies (RPBFMP), World Scientific Publishing Co. Pte. Ltd., vol. 22(01), pages 1-15, March.

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