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Quantification of operational risk: a scenario-based approach. (English) Zbl 1414.91156

Summary: In this article, I identify challenges to the loss distribution approach in modeling operational risk. I propose a scenario-based methodology for operational risk assessment, which recognizes that each risk can occur under a number of wide-ranging scenarios and that association between risks may behave differently for different scenarios. The model that is developed internally in the company provides a practical quantitative assessment of risk exposure that reflects a deep understanding of the company and its environment, making the risk calculation more responsive to the actual state, ensuring that the company is attending to its key operational risks. In this model qualitative and quantitative approaches are combined to build a loss distribution for individual and aggregate operational risk exposure. The model helps to portray the company’s internal control systems and aspects of business environment. These features can help the company increase its operational efficiency, reduce loss from undesirable incidents, and maintain the integrity of internal control.

MSC:

91B30 Risk theory, insurance (MSC2010)
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[1] Andersen, T. J.; Garvey, M.; Roggi, O., Managing Risk and Opportunity: The Governance of Strategic Risk-Taking (2014), New York: Oxford University Press, New York
[2] Burt, G.; Wright, G.; Bradfield, R.; Cairns, G.; Vander Heijden, K., 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 (2006)
[3] CEIOPS, CEIOPS Annual Report: Technical Specifications (2010)
[4] Chatterjee, S., On Combining Expert Opinion. Technical Report No. 49, SIAM Institute for Mathematics and Society, Stanford University (1981)
[5] Clemen, R. T., Calibration and the Aggregation of Probabilities, Management Science, 32, 312-314 (1986)
[6] Clemen, R. T.; Winkler, R. L., Combining Probability Distributions from Experts in Risk Analysis, Risk Analysis, 19, 187-203 (1999)
[7] Crouchy, M.; Galai, D.; Mark, R., The Essentials of Risk Management (2006), New York: Irwin/McGraw Hill, New York
[8] Druzdzel, M. J.; Van Der Gaag, L. C., Building Probabilistic Networks: Where Do the Numbers Come From?, IEEE Transactions on Knowledge and Data Engineering, 12, 481-486 (2000)
[9] Dupuis, D. J.; Jones, B. L., Multivariate Extreme Value Theory and Its Usefulness in Understanding Risk, North American Actuarial Journal, 10, 1-27 (2006) · Zbl 1480.91200
[10] Dutta, K. K.; Babbel, D. F., Scenario Analysis in the Measurement of Operational Risk Capital: A Change of Measure Approach, Journal of Risk and Insurance, 81, 303-334 (2014)
[11] EIOPA, Technical Specifications Part II on the Long-Term Guarantee Assessment Final Version. EIOPA/12/307 (2013)
[12] Embrechts, P.; Mcneil, A. J.; Straumann, D., Correlation: Pitfalls and Alternatives, Risk, 12, 69-71 (1999)
[13] Franzetti, C., Operational Risk Modelling and Management (2011), Philadelphia: Taylor & Francis Group, Philadelphia · Zbl 1213.91007
[14] Gatzert, N.; Kolb, A., Risk Measurement and Management of Operational Risk in Insurance Companies under Solvency II. . Friedrich-Alexander-University of Erlangen-Nuremberg, AFIR/ERM Colloquium 2012, Mexico City (2012)
[15] Gerardi, D.; Mclean, R.; Postlewaite, A., Aggregation of Expert Opinions, Games and Economic Behavior, 65, 339-371 (2009) · Zbl 1158.91335
[16] Girling, P.; Shimko, D. C.; Went, P., Operational Risk Management (2010), Jersey City, NJ: Global Association of Risk Professionals, Jersey City, NJ
[17] Hua, L.; Xia, M., Assessing High-Risk Scenarios by Full-Range Tail Dependence Copulas, North American Actuarial Journal, 18, 363-378 (2014) · Zbl 1414.91202
[18] International Actuarial Association, Note on Enterprise Risk Management for Capital and Solvency Purposes in the Insurance Industry (2009)
[19] Jones, P.; Robinson, P., Operations Management. (2012), Oxford, England: Oxford University Press, Oxford, England
[20] Jouini, M. N.; Clemen, R. T., Copula Models for Aggregating Expert Opinions, Operations Research, 44, 444-457 (1996) · Zbl 0864.90067
[21] Klugman, S. A.; Panjer, H. H.; Willmot, G. E., Loss Models from Data to Decisions. (2008), New York: Wiley, New York · Zbl 1159.62070
[22] Korb, K. B.; Nicholson, A. E., Bayesian Artificial Intelligence (2010), Boca Raton, FL: Chapman & Hall/CRC Press, Boca Raton, FL
[23] Mathur, S.; Baker, H. K.; Filbeck, G., Risk Aggregation and Capital Management, Investment Risk Management, 261-281 (2015), New York: Oxford University Press, New York
[24] OpRisk Advisory and Towers Perrin, A New Approach for Managing Operational Risk: Addressing the Issues Underlying the 2008 Global Financial Crisis (2009), Society of Actuaries
[25] Rosenberg, J. V.; Schuermann, T., A General Approach to Integrated Risk Management with Skewed, Fat-Tailed Risks, Journal of Financial Economics, 79, 569-614 (2006)
[26] Samad-Kahn, A.; Moncelet, B.; Pinch, T., Uses and Misuses of Loss Data (2006), Global Association of Risk Professionals
[27] Schlottmann, F.; Mitschele, A.; Seese, D.; Coello, C. A. C.; Aguirre, A. H.; Zitzler, E., A Multi-objective Approach to Integrated Risk Management, Evolutionary Multi-Criterion Optimization, 692-706 (2005), Berlin: Springer, Berlin · Zbl 1109.91364
[28] Scott, H.; Jackson, H., Operational Risk Insurance—Treatment under the New Basel Accord, International Finance Seminar (2002)
[29] Segal, S., Corporate Value of Enterprise Risk Management (2011), New York: Wiley, New York
[30] Shang, K.; Hossen, Z., Applying Fuzzy Logic to Risk Assessment and Decision-Making (2013), Schaumburg, IL: Society of Actuaries, Schaumburg, IL
[31] Shevchenko, P. V., Modelling Operational Risk Using Bayesian Inference (2011), Berlin: Springer, Berlin · Zbl 1213.91011
[32] Shevchenko, P. V.; Peters, G. W., Loss Distribution Approach for Operational Risk Capital Modelling under Basel II: Combining Different Data Sources for Risk Estimation, Journal of Governance and Regulation, 2, 33-57 (2013)
[33] Sweeting, P., Financial Enterprise Risk Management. (2011), Cambridge: Cambridge University Press, Cambridge · Zbl 1396.91005
[34] Tang, Q.; Yuan, Z., Asymptotic Analysis of the Loss Given Default in the Presence of Multivariate Regular Variation, North American Actuarial Journal, 17, 253-271 (2013) · Zbl 1412.91056
[35] Wang, R.; Peng, L.; Yang, J., CreditRisk+ Model with Dependent Risk Factors, North American Actuarial Journal, 19, 24-40 (2015) · Zbl 1414.91402
[36] Wilson, T. C., Value and Capital Management: A Handbook for the Finance and Risk Functions of Financial Institutions (2015), New York: Wiley, New York
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