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

  • Georges Hübner
  • Jean-Philippe Peters

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.

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Paper provided by ULB -- Universite Libre de Bruxelles in its series ULB Institutional Repository with number 2013/14158.

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Date of creation: 2008
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Publication status: Published in: Journal of Banking & Finance (2008) v.32,p.1049-1061
Handle: RePEc:ulb:ulbeco:2013/14158
Note: SCOPUS: ar.j
Contact details of provider: Postal: CP135, 50, avenue F.D. Roosevelt, 1050 Bruxelles
Web page: http://difusion.ulb.ac.be

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  1. Chavez-Demoulin, V. & Embrechts, P. & Neslehova, J., 2006. "Quantitative models for operational risk: Extremes, dependence and aggregation," Journal of Banking & Finance, Elsevier, vol. 30(10), pages 2635-2658, October.
  2. Hurlimann, Werner, 2004. "Fitting bivariate cumulative returns with copulas," Computational Statistics & Data Analysis, Elsevier, vol. 45(2), pages 355-372, March.
  3. 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.
  4. Drees, Holger & Kaufmann, Edgar, 1998. "Selecting the optimal sample fraction in univariate extreme value estimation," Stochastic Processes and their Applications, Elsevier, vol. 75(2), pages 149-172, July.
  5. Frachot, Antoine & Roncalli, Thierry & Salomon, Eric, 2004. "The Correlation Problem in Operational Risk," MPRA Paper 38052, University Library of Munich, Germany.
  6. François Longin, 2001. "Extreme Correlation of International Equity Markets," Journal of Finance, American Finance Association, vol. 56(2), pages 649-676, 04.
  7. Huisman, Ronald, et al, 2001. "Tail-Index Estimates in Small Samples," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(2), pages 208-16, April.
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