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The modelling of operational risk: experience with the analysis of the data collected by the Basel Committee

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Author Info
Marco Moscadelli () (Banca d'Italia)
Abstract

The 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.

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Paper provided by Bank of Italy, Economic Research Department in its series Temi di discussione (Economic working papers) with number 517.

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Date of creation: Jul 2004
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Handle: RePEc:bdi:wptemi:td_517_04

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Related research
Keywords: 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: General - - - Bayesian Analysis
C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Estimation
C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods
C19 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: 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 - - - Microeconomic Data
G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Mortgages
G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation

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  1. 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-.
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  2. Guiso, Luigi & Parigi, Giuseppe, 1996. "Investment and Demand Uncertainty," CEPR Discussion Papers 1497, C.E.P.R. Discussion Papers. [Downloadable!] (restricted)
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  3. 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.
  4. 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. [Downloadable!] (restricted)
  5. Luca Casolaro & Giorgio Gobbi, 2007. "Information Technology and Productivity Changes in the Banking Industry," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 36(1), pages 43-76, 02. [Downloadable!] (restricted)
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  7. Vries, Caspar de & Danielsson, Jon, 1996. "Tail Index and Quantile Estimation with Very High Frequency Data," CESifo Working Paper Series CESifo Working Paper No. , CESifo Group Munich.
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  1. Paul Embrechts & Giovanni Puccetti, 2006. "Bounds for Functions of Dependent Risks," Finance and Stochastics, Springer, vol. 10(3), pages 341-352, September. [Downloadable!] (restricted)
  2. Rosario Dell’Aquila & Paul Embrechts, 2006. "Extremes and Robustness: A Contradiction?," Financial Markets and Portfolio Management, Springer, vol. 20(1), pages 103-118, April. [Downloadable!] (restricted)
  3. 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. [Downloadable!]
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