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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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This chapter was published in:

  • Mark Carey & René M. Stulz, 2007. "The Risks of Financial Institutions," NBER Books, National Bureau of Economic Research, Inc, number care06-1, Ekim.
    This item is provided by National Bureau of Economic Research, Inc in its series NBER Chapters with number 9617.

    Handle: RePEc:nbr:nberch:9617

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    1. 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.
    2. 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.
    3. Huisman, Ronald, et al, 2001. "Tail-Index Estimates in Small Samples," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 19(2), pages 208-16, April.
    4. Beverly J. Hirtle, 2003. "What market risk capital reporting tells us about bank risk," Economic Policy Review, Federal Reserve Bank of New York, Federal Reserve Bank of New York, issue Sep, pages 37-54.
    5. Einmahl, J. & Dekkers, A. & de Haan, L., 1989. "A moment estimator for the index of an extreme-value distribution," Open Access publications from Tilburg University urn:nbn:nl:ui:12-125712, Tilburg University.
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    Cited by:
    1. Kapp, Daniel & Vega, Marco, 2012. "Real output costs of financial crises: a loss distribution approach," MPRA Paper 35706, University Library of Munich, Germany.
    2. Kapp, Daniel & Vega, Marco, 2012. "The Real Output Costs of Financial Crisis: A Loss Distribution Approach," Working Papers, Banco Central de Reserva del Perú 2012-013, Banco Central de Reserva del Perú.
    3. 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.
    4. Andreas Jobst, 2007. "Operational Risk," IMF Working Papers 07/239, International Monetary Fund.

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