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Standardized Measurement Approach for Operational risk: Pros and Cons

Author

Listed:
  • Gareth W. Peters

    (Department of Statistical Sciences - UCL - University College of London [London])

  • Pavel V. Shevchenko

    (Department of Statistical Sciences - UCL - University College of London [London], CSIRO - Commonwealth Scientific and Industrial Research Organisation [Canberra])

  • Bertrand K. Hassani

    (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique)

  • Ariane Chapelle

    (Department of Computer Science - UCL - University College of London [London])

Abstract

This response has been put together by academics and in total independence of any corporate or individual interests. Our results are solely driven by scientific analysis and presented in the interest of the financial and business community, both the regulated entities and the regulators alike. The response addresses the Standardised Measurement Approach (SMA) proposed in the Basel Committee for Banking Supervision consultative document "Standardised Measurement Approach for operational risk" (issued in March 2016 for comments by 3 June 2016) [BCBSd355,2016]; and closely related Operational risk Capital-at-Risk (OpCar) model proposed in the Committee consultative document "Operational risk - revisions to the simpler approaches" October 2014 [BCBSd291].

Suggested Citation

  • Gareth W. Peters & Pavel V. Shevchenko & Bertrand K. Hassani & Ariane Chapelle, 2016. "Standardized Measurement Approach for Operational risk: Pros and Cons," Post-Print halshs-01391062, HAL.
    • Handle: RePEc:hal:journl:halshs-01391062
    Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-01391062
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    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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).
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