Combining Internal Data with Scenario Analysis

DOI: 10.4236/me.2015.65055   PDF   HTML   XML   4,063 Downloads   4,516 Views  


A Bayesian inference approach offers a methodical concept that combines internal data with experts’ opinions. Joining these two elements with precision is certainly one of the challenges in operational risk. In this paper, we are interested in applying a Bayesian inference technique in a robust manner to be able to estimate a capital requirement that best approaches the reality. In addition, we illustrate the importance of a consistent scenario analysis in showing that the expert opinion coherence leads to a robust estimation of risk.

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Karam, E. and Planchet, F. (2015) Combining Internal Data with Scenario Analysis. Modern Economy, 6, 563-577. doi: 10.4236/me.2015.65055.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Shevchenko, P.V. (2011) Modelling Operational Risk Using Bayesian Inference. Springer, Berlin.
[2] Robert, C.P. (2007) The Bayesian Choice: From Decision-Theoretic Foundations to Computational Implementation second edition. Springer, Berlin.
[3] Solvency II (2010) Quantitative Impact Study 5 (Final Technical Specifications).
[4] Gamonet, J. (2006) Modélisation du risque opérationnel dans l’assurance. Mémoire d’actuaire, CEA.
[5] Berger, J.O. (1985) Statistical Decision Theory and Bayesian Analysis. 2nd Edition, Springer, New York (NY).
[6] Moosa, I.A. (2007) Operational Risk Management. Palgrave Macmillan, Basingstoke.
[7] Moosa, I.A. (2008) Quantification of Operational Risk under Basel II: The Good, Bad and Ugly. Palgrave Macmillan, Basingstoke.
[8] Santos, C.H., Krats, M. and Munoz, F.V. (2012) Modelling Macroeconomics Effects and Expert Judgements in Operational Risk: A Bayesian Approach. Research Center ESSEC Working Paper 1206.
[9] Frachot, A., Georges, P. and Roncalli, T. (2001) Loss Distribution Approach for Operational Risk. Groupe de Recherche Opérationelle, Crédit Lyonnais, France.
[10] Shevchenko, P.V. and Wüthrich, M.V. (2006) The Structural Modelling of Operational Risk via Bayesian Inference: Combining Loss Data with Expert Opinions. The Journal of Operational Risk, 1, 3-26.
[11] Jeffreys, H. (1946) An Invariant form for the Prior Probability in Estimation Problems. Proceedings of the Royal Society of London, Series A, Mathematical and Physical Sciences, 186, 453-461.
[12] Hastings, W.K. (1970) Monte Carlo Sampling Methods Using Markov Chains and Their Applications. Biometrika, 57, 97-109.
[13] Gilks, W.R., Richardson, S. and Spiegelhalter, D. (1996) Markov Chain Monte Carlo in Practice. Chapman & Hall, London.
[14] David, H.A. and Nagaraja, H.N. (2003) Order Statistics. 3th Edition. John Wiley & Sons Inc., New York.
[15] Buckland, S.T. (1985) Monte Carlo Confidence Intervals. Journal of Royal statistical society, Series C (Applied statistics), 34, 296-301.
[16] Basel Committee on Banking Supervision (2006) International Convergence of Capital Measurement and Capital Standards. BCBS, 144-146.
[17] Basel Committee on Banking Supervision (2002) Quantitative Impact Study 3 Technical Guidance.
[18] Tversky, A. and Kahneman, D. (1974) Judgment under Uncertainty: Heuristics and Biases. New Series, 185, 1124-1131.
[19] Lambrigger, D.D., Shevchenko, P.V. and Wüthrich, M.V. (2008) Data Combination under Basel II and Solvency 2: Operational Risk Goes Bayesian. Bulletin Francaisd’ Actuariat, 8, 4-13.

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