The modelling of operational risk: experience with the analysis of the data collected by the Basel Committee
AbstractThe revised Basel Capital Accord requires banks to meet a capital requirement for operational risk as part of an overall risk-based capital framework. Three distinct options for calculating operational risk charges are proposed (Basic Approach, Standardised Approach, Advanced Measurement Approaches), reflecting increasing levels of risk sensitivity. Since 2001, the Risk Management Group of the Basel Committee has been performing specific surveys of banksÂ’ operational loss data, with the main purpose of obtaining information on the industryÂ’s operational risk experience, to be used for the refinement of the capital framework and for the calibration of the regulatory coefficients. The second loss data collection was launched in the summer of 2002: the 89 banks participating in the exercise provided the Group with more than 47,000 observations, grouped by eight standardised Business Lines and seven Event Types. A summary of the data collected, which focuses on the description of the range of individualgross loss amounts and of the distribution of the banksÂ’ losses across the business lines/event types, was returned to the industry in March 2003. The objective of this paper is to move forward with respect to that document, by illustrating the methodologies and the outcomes of the inferential analysis carried out on the data collected through 2002. To this end, after pooling the individual banksÂ’ losses according to a Business Line criterion, the operational riskiness of each Business Line data set is explored using empirical and statistical tools. The work aims, first of all, to compare the sensitivity of conventional actuarial distributions and models stemming from the Extreme Value Theory in representing the highest percentiles of the data sets: the exercise shows that the extreme value model, in its Peaks Over Threshold representation, explains the behaviour of the operational risk data in the tail area well. Then, measures of severity and frequency of the large losses are gained and, by a proper combination of these estimates, a bottom-up operational risk capital figure is computed for each Business Line. Finally, for each Business Line and in the eight Business Lines as a whole, the contributions of the expected losses to the capital figures are evaluated and the relationships between the capital charges and the corresponding average level of Gross Incomes are determined and compared with the current coefficients envisaged in the simplified approaches of the regulatory framework.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by Bank of Italy, Economic Research and International Relations Area in its series Temi di discussione (Economic working papers) with number 517.
Date of creation: Jul 2004
Date of revision:
operational risk; heavy tails; conventional inference; Extreme Value Theory; Peaks Over Threshold; median shortfall; Point Process of exceedances; capital charge; Business Line; Gross Income; regulatory coefficients;
Find related papers by JEL classification:
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C19 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Other
- C29 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Other
- C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data
- G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
- G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation
This paper has been announced in the following NEP Reports:
- NEP-ALL-2006-01-24 (All new papers)
- NEP-FIN-2006-01-24 (Finance)
- NEP-FMK-2006-01-24 (Financial Markets)
- NEP-REG-2006-01-24 (Regulation)
- NEP-RMG-2006-01-24 (Risk Management)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Francis X. Diebold & Til Schuermann & John D. Stroughair, 1998.
"Pitfalls and Opportunities in the Use of Extreme Value Theory in Risk Management,"
New York University, Leonard N. Stern School Finance Department Working Paper Seires
98-081, New York University, Leonard N. Stern School of Business-.
- Francis X. Diebold & Til Schuermann & John D. Stroughair, 1998. "Pitfalls and Opportunities in the Use of Extreme Value Theory in Risk Management," Center for Financial Institutions Working Papers 98-10, Wharton School Center for Financial Institutions, University of Pennsylvania.
- Michel Dacorogna & Höskuldur Ari Hauksson & Thomas Domenig & Ulrich Müller & Gennady Samorodnitsky, 2001.
"Multivariate extremes, aggregation and risk estimation,"
CeNDEF Workshop Papers, January 2001
P2, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
- H. A. Hauksson & M. Dacorogna & T. Domenig & U. Mller & G. Samorodnitsky, 2001. "Multivariate extremes, aggregation and risk estimation," Quantitative Finance, Taylor and Francis Journals, vol. 1(1), pages 79-95.
- Gencay, Ramazan & Selcuk, Faruk, 2004. "Extreme value theory and Value-at-Risk: Relative performance in emerging markets," International Journal of Forecasting, Elsevier, vol. 20(2), pages 287-303.
- Christopher A. T. Ferro & Johan Segers, 2003. "Inference for clusters of extreme values," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(2), pages 545-556.
- Philippe Artzner & Freddy Delbaen & Jean-Marc Eber & David Heath, 1999. "Coherent Measures of Risk," Mathematical Finance, Wiley Blackwell, vol. 9(3), pages 203-228.
- McNeil, Alexander J. & Frey, Rudiger, 2000. "Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approach," Journal of Empirical Finance, Elsevier, vol. 7(3-4), pages 271-300, November.
- Rosario Dell’Aquila & Paul Embrechts, 2006. "Extremes and Robustness: A Contradiction?," Financial Markets and Portfolio Management, Springer, vol. 20(1), pages 103-118, April.
- Paul Embrechts & Giovanni Puccetti, 2006. "Bounds for Functions of Dependent Risks," Finance and Stochastics, Springer, vol. 10(3), pages 341-352, September.
- Georg Mainik & Ludger Rüschendorf, 2010. "On optimal portfolio diversification with respect to extreme risks," Finance and Stochastics, Springer, vol. 14(4), pages 593-623, December.
- Fantazzini, Dean, 2008. "Econometric Analysis of Financial Data in Risk Management (continuation). Section III: Managing Operational Risk," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 11(3), pages 87-122.
- Leonardo Gambacorta, 2004.
"How Do Banks Set Interest Rates?,"
NBER Working Papers
10295, National Bureau of Economic Research, Inc.
- 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.
- 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.
- Masako Ikefuji & Roger J. A. Laeven & Jan R. Magnus & Chris Muris, 2011. "Weitzman meets Nordhaus: Expected utility and catastrophic risk in a stochastic economy-climate model," ISER Discussion Paper 0825, Institute of Social and Economic Research, Osaka University.
- Robert Jarrow & Jeff Oxman & Yildiray Yildirim, 2010. "The cost of operational risk loss insurance," Review of Derivatives Research, Springer, vol. 13(3), pages 273-295, October.
- Chapelle, Ariane & Crama, Yves & Hübner, Georges & Peters, Jean-Philippe, 2008.
"Practical methods for measuring and managing operational risk in the financial sector: A clinical study,"
Journal of Banking & Finance,
Elsevier, vol. 32(6), pages 1049-1061, June.
- 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.
- Dominik D. Lambrigger & Pavel V. Shevchenko & Mario V. W\"uthrich, 2009. "The Quantification of Operational Risk using Internal Data, Relevant External Data and Expert Opinions," Papers 0904.1361, arXiv.org.
- Ojo, Marianne, 2007. "The role of the external auditor in bank regulation and supervision: A comparative analysis between the UK, Germany, Italy and the US," MPRA Paper 32614, University Library of Munich, Germany, revised Jan 2008.
- Ojo, Marianne, 2008. "The role of the external auditor in bank regulation and supervision: A comparative analysis," MPRA Paper 15747, University Library of Munich, Germany, revised Jun 2009.
- Embrechts, Paul & Neslehová, Johanna & Wüthrich, Mario V., 2009. "Additivity properties for Value-at-Risk under Archimedean dependence and heavy-tailedness," Insurance: Mathematics and Economics, Elsevier, vol. 44(2), pages 164-169, April.
- Helder Ferreira de Mendonça & Délio José Cordeiro Galvão & Renato Falci Villela Loures, 2010. "Estimation of Economic Capital Concerning Operational Risk in a Brazilian Banking Industry Case," Working Papers Series 213, Central Bank of Brazil, Research Department.
- Chernobai, Anna & Yildirim, Yildiray, 2008. "The dynamics of operational loss clustering," Journal of Banking & Finance, Elsevier, vol. 32(12), pages 2655-2666, December.
- Jarrow, Robert A., 2008. "Operational risk," Journal of Banking & Finance, Elsevier, vol. 32(5), pages 870-879, May.
- 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.
- Pavel V. Shevchenko & Grigory Temnov, 2009. "Modeling operational risk data reported above a time-varying threshold," Papers 0904.4075, arXiv.org, revised Jul 2009.
- Xiaolin Luo & Pavel V. Shevchenko & John B. Donnelly, 2009. "Addressing the Impact of Data Truncation and Parameter Uncertainty on Operational Risk Estimates," Papers 0904.2910, arXiv.org.
- Genest, Christian & Gerber, Hans U. & Goovaerts, Marc J. & Laeven, Roger J.A., 2009. "Editorial to the special issue on modeling and measurement of multivariate risk in insurance and finance," Insurance: Mathematics and Economics, Elsevier, vol. 44(2), pages 143-145, April.
- Barbu Teodora Cristina & Olteanu (Puiu) Ana Cornelia & Radu Alina Nicoleta, 2008. "The necessity of operational risk management and quantification," Annals of Faculty of Economics, University of Oradea, Faculty of Economics, vol. 3(1), pages 661-667, May.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ().
If references are entirely missing, you can add them using this form.