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Implications of alternative operational risk modeling techniques

Author

Listed:
  • Patrick de Fontnouvelle
  • John S. Jordan
  • Eric Rosengren
Abstract
Quantification of operational risk has received increased attention with the inclusion of an explicit capital charge for operational risk under the new Basle proposal. The proposal provides significant flexibility for banks to use internal models to estimate their operational risk, and the associated capital needed for unexpected losses. Most banks have used variants of value at risk models that estimate frequency, severity, and loss distributions. This paper examines the empirical regularities in operational loss data. Using loss data from six large internationally active banking institutions, we find that loss data by event types are quite similar across institutions. Furthermore, our results are consistent with economic capital numbers disclosed by some large banks, and also with the results of studies modeling losses using publicly available ?external? loss data.

Suggested Citation

  • Patrick de Fontnouvelle & John S. Jordan & Eric Rosengren, 2004. "Implications of alternative operational risk modeling techniques," Working Papers 04-9, Federal Reserve Bank of Boston.
    • Handle: RePEc:fip:fedbwp:04-9
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    References listed on IDEAS

    as
    1. Huisman, Ronald, et al, 2001. "Tail-Index Estimates in Small Samples," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(2), pages 208-216, April.
    2. Beverly Hirtle, 2003. "What market risk capital reporting tells us about bank risk," Economic Policy Review, Federal Reserve Bank of New York, issue Sep, pages 37-54.
    3. M.J.B. Hall, 1996. "The amendment to the capital accord to incorporate market risk," BNL Quarterly Review, Banca Nazionale del Lavoro, vol. 49(197), pages 271-277.
    4. Einmahl, J. H.J. & Dekkers, A. L.M. & de Haan, L., 1989. "A moment estimator for the index of an extreme-value distribution," Other publications TiSEM 81970cb3-5b7a-4cad-9bf6-2, Tilburg University, School of Economics and Management.
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    Citations

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    Cited by:

    1. Daniel Kapp & Marco Vega, 2014. "Real output costs of financial crises: A loss distribution approach," Cuadernos de Economía - Spanish Journal of Economics and Finance, Asociación Cuadernos de Economía, vol. 37(103), pages 13-28, Abril.
    2. Alina Mihaela Dima, 2009. "Operational Risk Assesement Tools for Quality Management in Banking Services," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 11(26), pages 364-372, June.
    3. Xiaoping Zhou & Rosella Giacometti & Frank J. Fabozzi & Ann H. Tucker, 2014. "Bayesian estimation of truncated data with applications to operational risk measurement," Quantitative Finance, Taylor & Francis Journals, vol. 14(5), pages 863-888, May.
    4. Paul Larsen, 2015. "Asyptotic Normality for Maximum Likelihood Estimation and Operational Risk," Papers 1508.02824, arXiv.org, revised Aug 2016.
    5. Tursunalieva, Ainura & Silvapulle, Param, 2016. "Nonparametric estimation of operational value-at-risk (OpVaR)," Insurance: Mathematics and Economics, Elsevier, vol. 69(C), pages 194-201.
    6. Albrecht, Peter & Schwake, Edmund & Winter, Peter, 2007. "Quantifizierung operationeller Risiken: Der Loss Distribution Approach," German Risk and Insurance Review (GRIR), University of Cologne, Department of Risk Management and Insurance, vol. 3(1), pages 1-45.
    7. Marco Flores, 2013. "Cuantificación del riesgo operacional mediante modelos de pérdidas agregadas y simulación de Monte Carlo," Analítika, Analítika - Revista de Análisis Estadístico/Journal of Statistical Analysis, vol. 5(1), pages 39-48, Junio.
    8. Valérie Chavez-Demoulin & Paul Embrechts & Marius Hofert, 2016. "An Extreme Value Approach for Modeling Operational Risk Losses Depending on Covariates," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 83(3), pages 735-776, September.
    9. Enrique Jiménez-Rodríguez & José Manuel Feria-Domínguez & Alonso Sebastián-Lacave, 2018. "Assessing the Health-Care Risk: The Clinical-VaR, a Key Indicator for Sound Management," IJERPH, MDPI, vol. 15(4), pages 1-17, March.
    10. Sinemis Zengin & Serhat Yuksel, 2016. "A Comparison of the Views of Internal Controllers/Auditors and Branch/Call Center Personnel of the Banks for Operational Risk: A Case for Turkish Banking Sector," International Journal of Finance & Banking Studies, Center for the Strategic Studies in Business and Finance, vol. 5(4), pages 10-29, July.
    11. Chapelle, Ariane & Crama, Yves & Hübner, Georges & Peters, Jean-Philippe, 2008. "Practical methods for measuring and managing operational risk in the financial sector: A clinical study," Journal of Banking & Finance, Elsevier, vol. 32(6), pages 1049-1061, June.
    12. Udo Milkau & Jürgen Bott, 2018. "Active Management of Operational Risk in the Regimes of the “Unknown”: What Can Machine Learning or Heuristics Deliver?," Risks, MDPI, vol. 6(2), pages 1-16, April.
    13. Andreas Jobst, 2007. "Consistent Quantitative Operational Risk Measurement and Regulation: Challenges of Model Specification, Data Collection and Loss Reporting," IMF Working Papers 2007/254, International Monetary Fund.
    14. S�verine Plunus & Georges Hübner & Jean-Philippe Peters, 2012. "Measuring operational risk in financial institutions," Applied Financial Economics, Taylor & Francis Journals, vol. 22(18), pages 1553-1569, September.
    15. Andreas Jobst, 2007. "Operational Risk: The Sting is Still in the Tail But the Poison Dependson the Dose," IMF Working Papers 2007/239, International Monetary Fund.
    16. Lu Wei & Jianping Li & Xiaoqian Zhu, 2018. "Operational Loss Data Collection: A Literature Review," Annals of Data Science, Springer, vol. 5(3), pages 313-337, September.
    17. Daniel Kapp & Marco Vega, 2012. "Real Output Costs of Financial Crises: A Loss Distribution Approach," Papers 1201.0967, arXiv.org, revised May 2012.
    18. Barbu Teodora Cristina & Olteanu (Puiu) Ana Cornelia & Radu Alina Nicoleta, 2008. "The necessity of operational risk management and quantification," Annals of Faculty of Economics, University of Oradea, Faculty of Economics, vol. 3(1), pages 661-667, May.

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    More about this item

    Keywords

    Bank capital; Risk management; Basel capital accord;
    All these keywords.

    JEL classification:

    • G2 - Financial Economics - - Financial Institutions and Services

    NEP fields

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