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Operational Risk Modelling: Focus on the Loss Distribution and Scenario-Based Approaches

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Operational Risk Management in Banks

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Abstract

The degree of flexibility that banks have had in operational risk modelling has fostered over the years the development of a variety of methods. Currently, these may be related to two categories, namely the loss distribution approach and the scenario-based approach. This chapter aims at analysing the specific features of both the methodologies, highlighting strengths and weaknesses of each one. As it is not possible to state which approach is the best in absolute terms, according to the best practices, the combined use could be the preferable choice.

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References

  • Aas, K. 2004. Modeling the dependence structure of financial assets: A survey of four copulas. Note, Norwegian Computing Centre, December.

    Google Scholar 

  • Abdymomunov, A., and F. Curti. 2016. Improving the robustness of operational risk estimation and stress testing through external data scaling, April. http://ssrn.com/abstract=2622175.

  • AG (Advanced Measurement Approaches Group). 2012. Scenario analysis: Part 2: Practices. Industry Position Paper.

    Google Scholar 

  • Aue, F., and M. Kalkbrener. 2007. LDA at work: Deutsche Bank’s approach to quantifying operational risk. Journal of Operational Risk 1 (4).

    Google Scholar 

  • Babbel, D.F., and K. Dutta. 2012. Scenario analysis in the measurement of operational risk capital: A change of measure approach. http://www.crai.com/publication/scenario-analysis-measurement-operational-risk-capital-change-measure-approach.

  • Bazzarello, D., and F. Piacenza. 2009. Il loss distribution approach e lo scenario based approach nell’esperienza UniCredit. In Il Rischio operativo nelle banche italiane, ed. G. Birindelli and P. Ferretti. Modelli, gestione e disclosure, Bancaria Editrice, Roma.

    Google Scholar 

  • BCBS (Basel Committee on Banking Supervision). 2009. Observed range of practice in key elements of Advanced Measurement Approaches (AMA), July.

    Google Scholar 

  • BCBS (Basel Committee on Banking Supervision). 2011. Operational risk—supervisory guidelines for the Advanced Measurement Approaches, June.

    Google Scholar 

  • BIS (Bank for International Settlements)-Committee on the Global Financial System. 2005. Stress testing at major financial institutions: Survey results and practice, Basel.

    Google Scholar 

  • Burt, G., G. Wright, R. Bradfield, G. Cairns, and Heijden K. Vander. 2006. The role of scenario planning in exploring the environment in view of the limitations of PEST and its derivatives. International Studies of Management and Organization 36: 50–76.

    Article  Google Scholar 

  • Chaudhary, D. 2014. Sensitivity analysis of scenario models for operational risk Advanced Measurement Approach. http://mpra.ub.uni-muenchen.de/60996/. MPRA Paper No. 60996.

  • Chapelle, A., Y. Crama, G. Hubner, and J.P. Peters. 2004. Basel II and Operational Risk: Implications for risk measurement and management in the financial sector, National Bank of Belgium, Working Paper.

    Google Scholar 

  • Chernobai, A.S., R. Svetlozar, and F.J. Fabozzi. 2005. Composite goodness-of-fit test for left truncated loss samples, Working Paper.

    Google Scholar 

  • Chernobai, A.S., S.T. Rachev, and F.J. Fabozzi. 2007. Operational risk: A guide to Basel II capital requirements, models and analysis. Willey.

    Google Scholar 

  • Clemen, R., and C. Fox. 2005. Subjective probability assessment in decision analysis: Partition dependence and bias toward the ignorance prior. Management Science 51 (9): 1417–1432.

    Article  Google Scholar 

  • Demarta, S., and A.J. McNeil. 2005. The t copula and related copulas. International Statistic Review 73 (1).

    Google Scholar 

  • Dutta, K., and J. Perry. 2013. A Tale of Tails: An empirical analysis of loss distribution models for estimating operational risk capital. Working Paper, Federal Reserve Bank of Boston, No. 6.

    Google Scholar 

  • Embrechts, P. 2000. Extremes and integrated risk management. Risk Books.

    Google Scholar 

  • Gregoriou, G.N. 2009. Operational risk toward Basel III. Wiley Finance.

    Google Scholar 

  • Guray Uner, S. 2008. Loss distribution approach for the operational risk economic capital. SAS Global Forum, Paper 163.

    Google Scholar 

  • Hosking, J.R.M., and J.R. Wallis. 1987. Parameter and quantile estimation for the generalized Pareto distribution. Technometrics 29 (3).

    Google Scholar 

  • Jenkinson, D. 2005. The Elicitation of probabilities—a review of the statistical literature’, Bayesian Elicitation of Experts’ Probabilities (BEEP). Working Paper, University of Sheffield, U.K.

    Google Scholar 

  • Kahneman, D., and A. Tversky. 1974. Judgement under Uncertainty: Heuristics and biases. Science 185: 1124–1131. September.

    Google Scholar 

  • Laycock, M. 2014. Operational risk quantification: Scenarios. Thomson Reuters Accelus, September.

    Google Scholar 

  • Lemmi Gigli, N. 2005. Trasferimento del Rischio Operativo. V Convention Aifirm, Convegno Nazionale Risk Management, Ottobre 20–21.

    Google Scholar 

  • Lubbe, J., and F. Snyman. 2010. The advanced measurement approach for banks. IFC Bulletin, (33), 141–149.

    Google Scholar 

  • McNeil, A.J., R. Frey, and P. Embrechts. 2005. Quantitative risk management. Princeton series in Finance.

    Google Scholar 

  • Mignola, G., and R. Ugoccioni. 2005. Tests of extreme value theory. Operational Risk 6 (10).

    Google Scholar 

  • Moscadelli, M. 2004. The modelling of operational risk experience with the analysis of the data collected by the Basel Committee. Banca d’Italia, Temi di discussione, n. 517.

    Google Scholar 

  • Oakley, J., and A. O’Hagan. 2004. Probability is perfect, but we can’t elicit it perfectly. Reliability Engineering and System Safety 85: 239–248.

    Article  Google Scholar 

  • OeNB (Oesterreichische Nationalbank), FMA (Austrian Financial Market Authority). 2006. Guidelines on Operational Risk Management.

    Google Scholar 

  • Rippel, M., and P. Teplý. 2008. Operational Risk—Scenario Analysis, Institute of Economic Studies, Faculty of Social Sciences. Charles University in Prague, IES Working Paper, No 15.

    Google Scholar 

  • Romano, C. 2002. Calibrating and simulating copula functions: An application to the Italian stock market. Working Paper, 12, Cidem.

    Google Scholar 

  • Romano, C., and A. Di Clemente. 2003. A copula extreme value theory approach for modeling operational risk. Working Paper.

    Google Scholar 

  • Savelli, N. 2005. L’approccio attuariale nella valutazione del rischio operativo: il Loss Distribution Approach e l’Extreme Value Theory. Corso di aggiornamento ASSBB—Università Cattolica su Il rischio operativo nelle banche: metodologie di valutazione e strumenti di copertura assicurativa, Milano, 7–8 Giugno.

    Google Scholar 

  • sbAMA (The scenario-based AMA) (sbAMA) working group (2003) “Scenario-based AMA”, May.

    Google Scholar 

  • Segal, S. 2011. Corporate value of enterprise risk management. Wiley.

    Google Scholar 

  • Shih, J., A. Samad-Khan, and P. Medapa. 2000. Is the size of an operational loss related to firm size? Operational Risk January.

    Google Scholar 

  • Sklar, A. 1959. Fonctions de repartition à n dimensions et leurs marges. Institute de Statistique de l’Université de Paris.

    Google Scholar 

  • Spetzler, C., and C.S. Von Holstein. 1975. Probability encoding in decision analysis. Management Science 22 (3): 340–357, November.

    Google Scholar 

  • Torresetti, R., and C. Nordio. 2014. Scaling operational loss data and its systemic risk implications, February. http://ssrn.com/abstract=2360483.

  • Watchorn, E. 2007. Applying a structured approach to operational risk scenario analysis in Australia. Information Paper, Australian Prudential Regulation Authority, September.

    Google Scholar 

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Authors and Affiliations

  1. Department of Management and Business Administration, “G. d’Annunzio” University of Chieti-Pescara, Pescara, Italy

    Giuliana Birindelli

  2. Department of Economics and Management, University of Pisa, Pisa, Italy

    Paola Ferretti

Authors
  1. Giuliana Birindelli
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  2. Paola Ferretti
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Correspondence to Paola Ferretti .

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Birindelli, G., Ferretti, P. (2017). Operational Risk Modelling: Focus on the Loss Distribution and Scenario-Based Approaches. In: Operational Risk Management in Banks. Palgrave Macmillan Studies in Banking and Financial Institutions. Palgrave Macmillan, London. https://doi.org/10.1057/978-1-137-59452-5_6

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  • DOI: https://doi.org/10.1057/978-1-137-59452-5_6

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  • Publisher Name: Palgrave Macmillan, London

  • Print ISBN: 978-1-137-59451-8

  • Online ISBN: 978-1-137-59452-5

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