IDEAS home Printed from https://ideas.repec.org/p/mse/cesdoc/16064.html
   My bibliography  Save this paper

Standardized Measurement Approach for Operational risk: Pros and Cons

Author

Listed:
  • Gareth W. Peters

    (Department of Statistical Sciences - University College London UK)

  • Pavel V. Shevchenko

    (CSIRO Australia)

  • Bertrand K. Hassani

    (Centre d'Economie de la Sorbonne, Grupo Santander)

  • Ariane Chapelle

    (Department of Computer Science - University College London UK)

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," Documents de travail du Centre d'Economie de la Sorbonne 16064, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    • Handle: RePEc:mse:cesdoc:16064
    as

    Download full text from publisher

    File URL: ftp://mse.univ-paris1.fr/pub/mse/CES2016/16064.pdf
    Download Restriction: no
    ---><---

    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).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Gareth W. Peters & Pavel V. Shevchenko & Bertrand K. Hassani & Ariane Chapelle, 2016. "Should the advanced measurement approach be replaced with the standardized measurement approach for operational risk?," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01391091, HAL.
    3. 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.
    4. Gareth W. Peters & Pavel V. Shevchenko & Bertrand K. Hassani & Ariane Chapelle, 2016. "Standardized Measurement Approach for Operational risk: Pros and Cons," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01391062, HAL.
    5. Gareth W. Peters & Pavel V. Shevchenko & Bertrand K. Hassani & Ariane Chapelle, 2016. "Should the advanced measurement approach be replaced with the standardized measurement approach for operational risk?," Post-Print halshs-01391091, HAL.
    6. Gareth W. Peters & Pavel V. Shevchenko & Bertrand K. Hassani & Ariane Chapelle, 2016. "Should the advanced measurement approach be replaced with the standardized measurement approach for operational risk?," Documents de travail du Centre d'Economie de la Sorbonne 16065, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    7. Matteo Malavasi & Gareth W. Peters & Pavel V. Shevchenko & Stefan Truck & Jiwook Jang & Georgy Sofronov, 2021. "Cyber Risk Frequency, Severity and Insurance Viability," Papers 2111.03366, arXiv.org, revised Mar 2022.
    8. Roc'io Paredes & Marco Vega, 2020. "An internal fraud model for operational losses in retail banking," Papers 2002.03235, arXiv.org.
    9. Klein, Nadja & Denuit, Michel & Lang, Stefan & Kneib, Thomas, 2014. "Nonlife ratemaking and risk management with Bayesian generalized additive models for location, scale, and shape," Insurance: Mathematics and Economics, Elsevier, vol. 55(C), pages 225-249.
    10. Malavasi, Matteo & Peters, Gareth W. & Shevchenko, Pavel V. & Trück, Stefan & Jang, Jiwook & Sofronov, Georgy, 2022. "Cyber risk frequency, severity and insurance viability," Insurance: Mathematics and Economics, Elsevier, vol. 106(C), pages 90-114.
    11. Yixuan Wang & Jianzhu Li & Ping Feng & Rong Hu, 2015. "A Time-Dependent Drought Index for Non-Stationary Precipitation Series," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(15), pages 5631-5647, December.
    12. Panayi, Efstathios & Peters, Gareth W. & Danielsson, Jon & Zigrand, Jean-Pierre, 2018. "Designating market maker behaviour in limit order book markets," Econometrics and Statistics, Elsevier, vol. 5(C), pages 20-44.
    13. Gauss Cordeiro & Josemar Rodrigues & Mário Castro, 2012. "The exponential COM-Poisson distribution," Statistical Papers, Springer, vol. 53(3), pages 653-664, August.
    14. Christian Kleiber & Achim Zeileis, 2016. "Visualizing Count Data Regressions Using Rootograms," The American Statistician, Taylor & Francis Journals, vol. 70(3), pages 296-303, July.
    15. Peters, Gareth W. & Shevchenko, Pavel V. & Young, Mark & Yip, Wendy, 2011. "Analytic loss distributional approach models for operational risk from the α-stable doubly stochastic compound processes and implications for capital allocation," Insurance: Mathematics and Economics, Elsevier, vol. 49(3), pages 565-579.
    16. Tong, Edward N.C. & Mues, Christophe & Thomas, Lyn, 2013. "A zero-adjusted gamma model for mortgage loan loss given default," International Journal of Forecasting, Elsevier, vol. 29(4), pages 548-562.
    17. Alexander, Carol & Cordeiro, Gauss M. & Ortega, Edwin M.M. & Sarabia, José María, 2012. "Generalized beta-generated distributions," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1880-1897.
    18. Pinho, Luis Gustavo B. & Nobre, Juvêncio S. & Singer, Julio M., 2015. "Cook’s distance for generalized linear mixed models," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 126-136.
    19. Zhang, Peng & Qiu, Zhenguo & Shi, Chengchun, 2016. "simplexreg: an R package for regression analysis of proportional data using the simplex distribution," LSE Research Online Documents on Economics 102115, London School of Economics and Political Science, LSE Library.
    20. Hofner, Benjamin & Mayr, Andreas & Schmid, Matthias, 2016. "gamboostLSS: An R Package for Model Building and Variable Selection in the GAMLSS Framework," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 74(i01).

    More about this item

    Keywords

    operational risk; standardized measurement approach; loss distribution approach; advanced measurement approach; Basel Committee for Banking Supervision regulations;
    All these keywords.

    JEL classification:

    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:mse:cesdoc:16064. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Lucie Label (email available below). General contact details of provider: https://edirc.repec.org/data/cenp1fr.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.