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Practical methods for measuring and managing operational risk in the financial sector: a clinical study

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  • Georges Hübner
  • Jean-Philippe Peters

Abstract

This paper analyzes the implications of the advanced measurement approach (AMA) for the assessment of operational risk. Through a clinical case study on a matrix of two selected business lines and two event types of a large financial institution, we develop a procedure that addresses the major issues faced by banks in the implementation of the AMA. For each cell, we calibrate two truncated distributions functions, one for "normal" losses and the other for the "extreme" losses. In addition, we propose a method to include external data in the framework. We then estimate the impact of operational risk management on bank profitability, through an adapted measure of RAROC. The results suggest that substantial savings can be achieved through active management techniques. © 2007 Elsevier B.V. All rights reserved.

Suggested Citation

  • Georges Hübner & Jean-Philippe Peters, 2008. "Practical methods for measuring and managing operational risk in the financial sector: a clinical study," ULB Institutional Repository 2013/14158, ULB -- Universite Libre de Bruxelles.
    • Handle: RePEc:ulb:ulbeco:2013/14158
    Note: SCOPUS: ar.j
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    1. Patrick de Fontnouvelle & Eric Rosengren & John Jordan, 2007. "Implications of Alternative Operational Risk Modeling Techniques," NBER Chapters, in: The Risks of Financial Institutions, pages 475-505, National Bureau of Economic Research, Inc.
    2. Marco Moscadelli, 2004. "The modelling of operational risk: experience with the analysis of the data collected by the Basel Committee," Temi di discussione (Economic working papers) 517, Bank of Italy, Economic Research and International Relations Area.
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    Cited by:

    1. Stefan Mittnik & Sandra Paterlini & Tina Yener, 2011. "Operational–risk Dependencies and the Determination of Risk Capital," Center for Economic Research (RECent) 070, University of Modena and Reggio E., Dept. of Economics "Marco Biagi".
    2. Yuqian Xu & Lingjiong Zhu & Michael Pinedo, 2020. "Operational Risk Management: A Stochastic Control Framework with Preventive and Corrective Controls," Operations Research, INFORMS, vol. 68(6), pages 1804-1825, November.
    3. Marco Rocco, 2011. "Extreme value theory for finance: a survey," Questioni di Economia e Finanza (Occasional Papers) 99, Bank of Italy, Economic Research and International Relations Area.
    4. Eling, Martin & Loperfido, Nicola, 2017. "Data breaches: Goodness of fit, pricing, and risk measurement," Insurance: Mathematics and Economics, Elsevier, vol. 75(C), pages 126-136.
    5. 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.
    6. Dahen, Hela & Dionne, Georges, 2010. "Scaling models for the severity and frequency of external operational loss data," Journal of Banking & Finance, Elsevier, vol. 34(7), pages 1484-1496, July.
    7. Dahen, Hela & Dionne, Georges & Zajdenweber, Daniel, 2010. "Extremal events in a bank operational losses," Working Papers 10-2, HEC Montreal, Canada Research Chair in Risk Management.
    8. Tyrone Lin & Chia-Chi Lee & Yu-Chuan Kuan, 2013. "The optimal operational risk capital requirement by applying the advanced measurement approach," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 21(1), pages 85-101, January.
    9. 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.
    10. Feria-Domínguez, José Manuel & Jiménez-Rodríguez, Enrique & Sholarin, Ola, 2015. "Tackling the over-dispersion of operational risk: Implications on capital adequacy requirements," The North American Journal of Economics and Finance, Elsevier, vol. 31(C), pages 206-221.
    11. Thomas Conlon & Xing Huan & Steven Ongena, 2020. "Operational Risk Capital," Swiss Finance Institute Research Paper Series 20-55, Swiss Finance Institute.
    12. Georges Dionne & Samir Saissi Hassani, . "Hidden Markov regimes in operational loss data: application to the recent financial crisis," Journal of Operational Risk, Journal of Operational Risk.
    13. Ajjima Jiravichai & Ruth Banomyong, 2022. "A Proposed Methodology for Literature Review on Operational Risk Management in Banks," Risks, MDPI, vol. 10(5), pages 1-18, May.
    14. Elshahat, A. & Parhizgari, Ali & Hong, Liang, 2012. "The information content of the Banking Regulatory Agencies and the Depository Credit Intermediation Institutions," Journal of Economics and Business, Elsevier, vol. 64(1), pages 90-104.
    15. Groll, Andreas & Hambuckers, Julien & Kneib, Thomas & Umlauf, Nikolaus, 2019. "LASSO-type penalization in the framework of generalized additive models for location, scale and shape," Computational Statistics & Data Analysis, Elsevier, vol. 140(C), pages 59-73.
    16. Uddin, Md Hamid & Mollah, Sabur & Islam, Nazrul & Ali, Md Hakim, 2023. "Does digital transformation matter for operational risk exposure?," Technological Forecasting and Social Change, Elsevier, vol. 197(C).
    17. Yinhong Yao & Jianping Li, 2022. "Operational risk assessment of third-party payment platforms: a case study of China," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-20, December.
    18. Chang, Carolyn W. & Chang, Jack S.K. & Lu, WeLi, 2010. "Pricing catastrophe options with stochastic claim arrival intensity in claim time," Journal of Banking & Finance, Elsevier, vol. 34(1), pages 24-32, January.
    19. Wang, Zongrun & Wang, Wuchao & Chen, Xiaohong & Jin, Yanbo & Zhou, Yanju, 2012. "Using BS-PSD-LDA approach to measure operational risk of Chinese commercial banks," Economic Modelling, Elsevier, vol. 29(6), pages 2095-2103.
    20. Lu, Zhaoyang, 2011. "Modeling the yearly Value-at-Risk for operational risk in Chinese commercial banks," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 82(4), pages 604-616.
    21. Guasti Lima Fabiano & Castro Junior Sant Clair de & Pimenta Júnior Tabajara & Gaio Luiz Eduardo, 2014. "Performance of the different RAROC models and their relation with the creation of economic value: A study of the largest banks operating in Brazil," Contaduría y Administración, Accounting and Management, vol. 59(4), pages 87-104, octubre-d.
    22. Ming-Tao CHUNG & Ming-Hua HSIEH & Yan-Ping CHI, 2017. "Computation of Operational Risk for Financial Institutions," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 77-87, September.
    23. Brechmann, Eike & Czado, Claudia & Paterlini, Sandra, 2014. "Flexible dependence modeling of operational risk losses and its impact on total capital requirements," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 271-285.

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