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Implications of Alternative Operational Risk Modeling Techniques

In: The Risks of Financial Institutions

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  • Patrick de Fontnouvelle
  • Eric Rosengren
  • John Jordan

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.
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Suggested Citation

  • Patrick de Fontnouvelle & Eric Rosengren & John Jordan, 2007. "Implications of Alternative Operational Risk Modeling Techniques," NBER Chapters,in: The Risks of Financial Institutions, pages 475-512 National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberch:9617
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    References listed on IDEAS

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    1. M.J.B. Hall, 1996. "The amendment to the capital accord to incorporate market risk," Banca Nazionale del Lavoro Quarterly Review, Banca Nazionale del Lavoro, vol. 49(197), pages 271-277.
    2. 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.
    3. 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.
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    Cited by:

    1. 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.
    2. Daniel Kapp & Marco Vega, 2012. "Real Output Costs of Financial Crises: A Loss Distribution Approach," Papers 1201.0967, arXiv.org, revised May 2012.
    3. Tursunalieva, Ainura & Silvapulle, Param, 2016. "Nonparametric estimation of operational value-at-risk (OpVaR)," Insurance: Mathematics and Economics, Elsevier, vol. 69(C), pages 194-201.
    4. Kapp, Daniel & Vega, Marco, 2012. "The Real Output Costs of Financial Crisis: A Loss Distribution Approach," Working Papers 2012-013, Banco Central de Reserva del Perú.
    5. 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.
    6. Andreas Jobst, 2007. "Operational Risk; The Sting is Still in the Tail But the Poison Dependson the Dose," IMF Working Papers 07/239, International Monetary Fund.
    7. Séverine 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.

    More about this item

    JEL classification:

    • G2 - Financial Economics - - Financial Institutions and Services

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