Advertisement

Operational Risk Measurement: A Literature Review

  • Francesco GiannoneEmail author
Chapter
Part of the Palgrave Macmillan Studies in Banking and Financial Institutions book series (SBFI)

Abstract

Operational measurement is not the only target of the overall operational risk management process, but it is a fundamental phase as it defines its efficiency; furthermore the need to measure operational risk comes from the capital regulatory framework. Taking this into account, the chapter describes and compares the different methods used to measure operational risk, both by practitioners and by academics: Loss Distribution Approach (LDA), scenario analysis and Bayesian methods. The majority of the advanced banks calculate capital requirement through LDA: the chapter focuses on how it works, analysing in detail the different phases of which it is composed and its applications, in particular the Extreme Value Theory (EVT), which is the most popular one.

Keywords

Loss distribution approach Extreme value theory Scenario analysis Operational risk literature 

References

  1. Alexander, C. (2000). Bayesian methods for measuring operational risk. Discussion Papers in Finance. Henley Business School, Reading University.Google Scholar
  2. Alexander, C. (2003). Managing operational risks with Bayesian networks. Operational Risk: Regulation, Analysis and Management, 1, 285–294.Google Scholar
  3. Amin, Z. (2016). Quantification of operational risk: A scenario-based approach. North American Actuarial Journal, 20(3), 286–297.CrossRefGoogle Scholar
  4. Aquaro, V., Bardoscia, M., Bellotti, R., Consiglio, A., De Carlo, F., & Ferri, G. (2010). A Bayesian networks approach to operational risk. Physica A: Statistical Mechanics and Its Applications, 389(8), 1721–1728.CrossRefGoogle Scholar
  5. BCBS. (2001). Operational risk. Supporting document to the New Basel Capital Accord. Basel Committee on Banking Supervision, Consultative Document. https://www.bis.org/publ/bcbsca07.pdf.
  6. BCBS. (2004). International convergence of capital measurement and capital standards. Basel Committee on Banking Supervision. http://www.bis.org/publ/bcbs107.htm.
  7. BCBS. (2009). Results from the 2008 loss data collection exercise for operational risk. Basel Committee on Banking Supervision. www.bis.org/publ/bcbs160a.pdf.
  8. BCBS. (2011). Operational risk—Supervisory guidelines for the advanced measurement approaches. Basel Committee on Banking Supervision. www.bis.org/publ/bcbs196.htm.
  9. Bee, M. (2005). Copula-based multivariate models with applications to risk management and insurance. University of Trento: Department of Economics Working Paper.Google Scholar
  10. Böcker, K., & Klüppelberg, C. (2008). Modelling and measuring multivariate operational risk with Lévy copulas. The Journal of Operational Risk, 3(2), 3–27.CrossRefGoogle Scholar
  11. Cavallo, A., Rosenthal, B., Wang, X., & Yan, J. (2012). Treatment of the data collection threshold in operational risk: case study with the lognormal distribution. The Journal of Operational Risk, 7(1).Google Scholar
  12. Chavez-Demoulin, V., & Embrechts, P. (2004a). Advanced extremal models for operational risk (p. 4). ETH, Zurich: Department of Mathematics.Google Scholar
  13. Chavez-Demoulin, V., & Embrechts, P. (2004b). Smooth extremal models in finance and insurance. Journal of Risk and Insurance, 71(2), 183–199.CrossRefGoogle Scholar
  14. Chavez-Demoulin, V., Embrechts, P., & Hofert, M. (2016). An extreme value approach for modeling operational risk losses depending on covariates. Journal of Risk & Insurance, 83(3), 735–776.CrossRefGoogle Scholar
  15. Chavez-Demoulin, V., Embrechts, P., & Nešlehová, J. (2006). Quantitative models for operational risk: Extremes, dependence and aggregation. Journal of Banking & Finance, 30(10), 2635–2658.CrossRefGoogle Scholar
  16. Cornalba, C., & Giudici, P. (2004). Statistical models for operational risk management. Physical A: Statistical Mechanics and Its Applications, 338(1), 166–172.CrossRefGoogle Scholar
  17. Daneshkhah, A. R. (2004). Uncertainty in probabilistic risk assessment: A review. The University of Sheffield, August 9.Google Scholar
  18. Degen, M., Embrechts, P., & Lambrigger, D. D. (2007). The quantitative modeling of operational risk: Between g-and-h and EVT. Astin Bulletin, 37(02), 265–291.Google Scholar
  19. Dionne, G., & Dahen, H. (2007). What about Underevaluating Operational Value at Risk in the Banking Sector? Cahier de recherche/Working Paper, 7, 23.Google Scholar
  20. Dutta, K. K., & Babbel, D. F. (2014). Scenario analysis in the measurement of operational risk capital: A change of measure approach. Journal of Risk and Insurance, 81(2), 303–334.CrossRefGoogle Scholar
  21. Dutta, K., & Perry, J. (2006). A tale of tails: An empirical analysis of loss distribution models for estimating operational risk capital (Working paper series). Federal Reserve Bank of Boston, pp. 6–13.Google Scholar
  22. Embrechts, P., & Puccetti, G. (2008). Aggregating risk across matrix structured loss data: The case of operational risk. Journal of Operational Risk, 3(2), 29–44.CrossRefGoogle Scholar
  23. Embrechts, P., Furrer, H., & Kaufmann, R. (2003). Quantifying regulatory capital for operational risk. Derivatives Use, Trading and Regulation, 9(3), 217–233.Google Scholar
  24. Embrechts, P., Klüppelberg, C., & Mikosch, T. (1997). Modelling extremal events. Applications of Mathematics, 33.Google Scholar
  25. Embrechts, P., Resnick, S. I., & Samorodnitsky, G. (1999). Extreme value theory as a risk management tool. North American Actuarial Journal, 3(2), 30–41.CrossRefGoogle Scholar
  26. Figini, S., Gao, L., & Giudici, P. (2013). Bayesian operational risk model. University of Pavia: Department of Economics and Management, 47.Google Scholar
  27. Fox, C. R., & Clemen, R. T. (2005). Subjective probability assessment in decision analysis: Partition dependence and bias toward the ignorance prior. Management Science, 51(9), 1417–1432.CrossRefGoogle Scholar
  28. Frachot, A., Georges, P., & Roncalli, T. (2001). Loss distribution approach for operational ris’. Credit Lyonnais.Google Scholar
  29. Frachot, A., Roncalli, T., & Salomon, E. (2004). The correlation problem in operational risk (p. 38052). Germany: University Library of Munich.Google Scholar
  30. Giacometti, R., Rachev, S., Chernobai, A., & Bertocchi, M. (2008). Aggregation issues in operational risk. Journal of Operational Risk, 3(3), 3–23.CrossRefGoogle Scholar
  31. Giudici, P. (2004). Integration of qualitative and quantitative operational risk data: A Bayesian approach. Operational Risk Modelling and Analysis, Theory and Practice, RISK Books, London, pp. 131–138.Google Scholar
  32. Guegan, D., & Hassani, B. K. (2013). Operational risk: A Basel II++step before Basel III. Journal of Risk Management in Financial Institutions, 6(1), pp. 37–53.Google Scholar
  33. Guillen, M., Gustafsson, J., Nielsen, J. P., & Pritchard, P. (2007). Using external data in operational risk. The Geneva Papers on Risk and Insurance Issues and Practice, 32(2), 178–189.CrossRefGoogle Scholar
  34. Gustafsson, J., & Nielsen, J. P. (2008). A mixing model for operational risk. Journal of Operational Risk, 3(3), 25–38.CrossRefGoogle Scholar
  35. Heckman, P. E., & Meyers, G. G. (1983). The calculation of aggregate loss distributions from claim severity and claim count distributions. In Proceedings of the Casualty Actuarial Society, 70, 133–134.Google Scholar
  36. Heideman, M., Johnson, D., & Burrus, C. (1984). Gauss and the history of the fast Fourier transform. IEEE ASSP Magazine, 1(4), 14–21.Google Scholar
  37. Hillson, D. A., & Hulett, D. T. (2004). Assessing risk probability: Alternative approaches (pp. 1–5). PMI Global Congress Proceeding: Prague, Czech Republic.Google Scholar
  38. Jobst, A. (2007). Operational risk: The sting is still in the tail but the poison depends on the dose. International Monetary Fund, pp. 7–239.Google Scholar
  39. Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica: Journal of the Econometric Society, 263–291.Google Scholar
  40. Klugman, S. A., Panjer, H. H., & Willmot, G. E. (2008). Loss models: From data to decisions. John Wiley & Sons.Google Scholar
  41. Lambrigger, D. D., Shevchenko, P. V., & Wuthrich, M. V. (2007). The quantification of operational risk using internal data, relevant external data and expert opinion. Journal of Operational Risk, 2(3), 3–28.CrossRefGoogle Scholar
  42. Larsen, P. (2015). Operational risk models and maximum likelihood estimation error for small sample-sizes. arXivpreprintarXiv:1508.02824.Google Scholar
  43. Leadbetter, M. R. (1991). On a basis for peaks over threshold modeling. Statistics & Probability Letters, 12(4), 357–362.CrossRefGoogle Scholar
  44. Lindskog, F., & McNeil, A. J. (2003). Common Poisson shock models: Applications to insurance and credit risk modelling. Astin Bulletin, 33(02), 209–238.CrossRefGoogle Scholar
  45. Luo, X., Shevchenko, P. V., & Donnelly, J. B. (2007). Addressing the impact of data truncation and parameter uncertainty on operational risk estimates. Journal of Operational Risk, 2(4), 3–27.CrossRefGoogle Scholar
  46. Moscadelli, M. (2004). The modelling of operational risk: Experience with the analysis of the data collected by the Basel Committee. Bank of Italy: Economic Research and International Relations Area, 517.Google Scholar
  47. Moscadelli, M., Chernobai, A., & Rachev, S. T. (2005). Treatment of incomplete data in the field of operational risk: The effects on parameter estimates, EL, and UL figures. Operational Risk, 6, 28–34.Google Scholar
  48. Neil, M., Fenton, N., & Tailor, M. (2005). Using Bayesian networks to model expected and unexpected operational losses. Risk Analysis, 25(4), 963–972.CrossRefGoogle Scholar
  49. Neil, M., Häger, D., & Andersen, L. B. (2009). Modeling operational risk in financial institutions using hybrid dynamic Bayesian networks. The Journal of Operational Risk, 4(1), 3.CrossRefGoogle Scholar
  50. Pakhchanyan, S. (2016). Operational risk management in financial institutions: A literature review. International Journal of Financial Studies, 4(4), 20.Google Scholar
  51. Panjer, H. H. (1981). Recursive evaluation of a family of compound distributions. ASTIN Bulletin, 12(01), 22–26.Google Scholar
  52. Peters, G. W., & Sisson, S. A. (2006). Bayesian inference, Monte Carlo sampling and operational risk. Journal of Operational Risk, 1(3), 27–50.CrossRefGoogle Scholar
  53. Peters, G. W., Shevchenko, P. V., & Wuthrich, M. V. (2009). Dynamic operational risk: Modelling dependence and combining different sources of information. The Journal of Operational Risk, 4(2), 69–104.CrossRefGoogle Scholar
  54. Powojowski, M. R., Reynolds, D., & Tuenter, H. J. (2002). Dependent events and operational risk. Algo Research Quarterly, 5(2), 65–73.Google Scholar
  55. Rippel, M., & Teply, P. (2011). Operational Risk-Scenario Analysis. Prague Economic Papers, 1, 23–39.CrossRefGoogle Scholar
  56. Rozenfeld, I. (2010). Using shifted distributions in computing operational risk capital. Available at SSRN.Google Scholar
  57. Santos, H. C., Kratz, M., & Munoz, F. M. (2012). Modelling macroeconomic effects and expert judgments in operational risk: A Bayesian approach. The Journal of Operational Risk, 7(4), 3.CrossRefGoogle Scholar
  58. Scenario Based AMA Working Group. (2003). Scenario-based AMA. Working paper, London.Google Scholar
  59. Shevchenko, P. V. (2010). Implementing loss distribution approach for operational risk. Applied Stochastic Models in Business and Industry, 26(3), 277–307.CrossRefGoogle Scholar
  60. Shevchenko, P. V., & Peters, G. W. (2013). Loss distribution approach for operational risk capital modelling under Basel II: Combining different data sources for risk estimation. arXiv preprint arXiv:1306.1882.
  61. Sklar, M. (1959). Fonctions de répartition à n dimensions et leurs marges (p. 8). No: Université Paris.Google Scholar
  62. Sundt, B., & Jewell, W. S. (1981). Further results on recursive evaluation of compound distributions. ASTIN Bulletin: The Journal of the IAA, 12(1), 27–39.Google Scholar
  63. Svensson, K. P. (2015). A Bayesian Approach to Modelling Operational Risk When Data is Scarce (Working Paper).Google Scholar
  64. Tversky, A., & Kahneman, D. (1973). Availability: A heuristic for judging frequency and probability. Cognitive Psychology, 5(2), 207–232.CrossRefGoogle Scholar
  65. Tversky, A., & Kahneman, D. (1975). Judgment under uncertainty: Heuristics and biases. Utility, probability, and human decision making. Springer Netherlands, pp. 141–162.Google Scholar
  66. Watchorn, E. (2007). Applying a structured approach to operational risk scenario analysis in Australia. APRA.Google Scholar
  67. Zhou, Y., Fenton, N., & Neil, M. (2014). Bayesian network approach to multinomial parameter learning using data and expert judgments. International Journal of Approximate Reasoning, 55(5), 1252–1268.CrossRefGoogle Scholar

Copyright information

© The Author(s) 2018

Authors and Affiliations

  1. 1.Sapienza UniversityRomeItaly

Personalised recommendations