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Implementing loss distribution approach for operational risk

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  • Pavel V. Shevchenko
Abstract
In order to quantify the operational risk capital charge under the current regulatory framework for banking supervision, referred to as Basel II, many banks adopt the loss distribution approach. There are many modeling issues that should be resolved to use this approach in practice. In this paper we review the quantitative methods suggested in the literature for the implementation of the approach. In particular, the use of Bayesian inference that allows one to take expert judgement and parameter uncertainty into account, modeling dependence, and inclusion of insurance are discussed. Copyright © 2009 John Wiley & Sons, Ltd.

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  • Pavel V. Shevchenko, 2010. "Implementing loss distribution approach for operational risk," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 26(3), pages 277-307, May.
    • Handle: RePEc:wly:apsmbi:v:26:y:2010:i:3:p:277-307
    DOI: 10.1002/asmb.812
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    References listed on IDEAS

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    12. Marco Bee, 2005. "On maximum likelihood estimation of operational loss distributions," Department of Economics Working Papers 0503, Department of Economics, University of Trento, Italia.
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    Cited by:

    1. Gareth W. Peters & Aaron D. Byrnes & Pavel V. Shevchenko, 2010. "Impact of Insurance for Operational Risk: Is it worthwhile to insure or be insured for severe losses?," Papers 1010.4406, arXiv.org, revised Nov 2010.
    2. Xiaolin Luo & Pavel V. Shevchenko, 2012. "Bayesian Model Choice of Grouped t-Copula," Methodology and Computing in Applied Probability, Springer, vol. 14(4), pages 1097-1119, December.
    3. Ramírez-Cobo, Pepa & Carrizosa, Emilio & Lillo, Rosa E., 2021. "Analysis of an aggregate loss model in a Markov renewal regime," Applied Mathematics and Computation, Elsevier, vol. 396(C).
    4. Peter Mitic, 2017. "Conduct Risk - distribution models with very thin Tails," Papers 1705.06868, arXiv.org.
    5. Eling, Martin & Jung, Kwangmin, 2018. "Copula approaches for modeling cross-sectional dependence of data breach losses," Insurance: Mathematics and Economics, Elsevier, vol. 82(C), pages 167-180.
    6. Peters, Gareth W. & Byrnes, Aaron D. & Shevchenko, Pavel V., 2011. "Impact of insurance for operational risk: Is it worthwhile to insure or be insured for severe losses?," Insurance: Mathematics and Economics, Elsevier, vol. 48(2), pages 287-303, March.
    7. Paul Larsen, 2015. "Asyptotic Normality for Maximum Likelihood Estimation and Operational Risk," Papers 1508.02824, arXiv.org, revised Aug 2016.
    8. Xu Zhao & Zhongxian Zhang & Weihu Cheng & Pengyue Zhang, 2019. "A New Parameter Estimator for the Generalized Pareto Distribution under the Peaks over Threshold Framework," Mathematics, MDPI, vol. 7(5), pages 1-18, May.
    9. Lina M Cortés & Juan F. Rendón & Javier Perote, 2021. "Determining the banking solvency risk in times of COVID-19 through Gram-Charlier expansions," Documentos de Trabajo de Valor Público 19593, Universidad EAFIT.
    10. Bakhodir Ergashev & Konstantin Pavlikov & Stan Uryasev & Evangelos Sekeris, 2016. "Estimation of Truncated Data Samples in Operational Risk Modeling," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 83(3), pages 613-640, September.
    11. Wang, Zongrun & Wang, Wuchao & Chen, Xiaohong & Jin, Yanbo & Zhou, Yanju, 2012. "Using BS-PSD-LDA approach to measure operational risk of Chinese commercial banks," Economic Modelling, Elsevier, vol. 29(6), pages 2095-2103.
    12. Pavel V. Shevchenko, 2010. "Calculation of aggregate loss distributions," Papers 1008.1108, arXiv.org.

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