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The dynamics of operational loss clustering

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  • Chernobai, Anna
  • Yildirim, Yildiray
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    Abstract

    This paper investigates the characteristics of the operational loss data formation mechanism that takes place between the date of discovery of a new operational risk event and the final settlement date on which all losses are materialized. The first loss that characterizes the initial impact of a new operational risk event frequently triggers a sequence of related losses. Then, losses generated by the same event are not independent and follow a predictable scheme and the frequency of secondary losses is not homogeneous: both are functions of the initial loss amount and time. We model the arrival intensity and loss severities with a shot-noise stochastic process and derive its key properties. We then discuss implications of our model for the estimation of the regulatory capital charge for operational risk. In an empirical analysis, we find strong evidence of a shot-noise behavior in operational losses using the data of a major US commercial bank.

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    Bibliographic Info

    Article provided by Elsevier in its journal Journal of Banking & Finance.

    Volume (Year): 32 (2008)
    Issue (Month): 12 (December)
    Pages: 2655-2666

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    Handle: RePEc:eee:jbfina:v:32:y:2008:i:12:p:2655-2666

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    Web page: http://www.elsevier.com/locate/jbf

    Related research

    Keywords: Operational risk Basel II Shot noise Cox process Aggregate loss;

    References

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    1. de Fontnouvelle, Patrick & Dejesus-Rueff, Virginia & Jordan, John S. & Rosengren, Eric S., 2006. "Capital and Risk: New Evidence on Implications of Large Operational Losses," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 38(7), pages 1819-1846, October.
    2. Mark Carey & René M. Stulz, 2007. "The Risks of Financial Institutions," NBER Books, National Bureau of Economic Research, Inc, number care06-1, October.
    3. Chavez-Demoulin, V. & Embrechts, P. & Neslehova, J., 2006. "Quantitative models for operational risk: Extremes, dependence and aggregation," Journal of Banking & Finance, Elsevier, vol. 30(10), pages 2635-2658, October.
    4. Marco Moscadelli, 2004. "The modelling of operational risk: experience with the analysis of the data collected by the Basel Committee," Temi di discussione (Economic working papers) 517, Bank of Italy, Economic Research and International Relations Area.
    5. Rosenberg, Joshua V. & Schuermann, Til, 2006. "A general approach to integrated risk management with skewed, fat-tailed risks," Journal of Financial Economics, Elsevier, vol. 79(3), pages 569-614, March.
    6. Angelos Dassios & Jiwook Jang, 2003. "Pricing of catastrophe reinsurance and derivatives using the Cox process with shot noise intensity," LSE Research Online Documents on Economics 2849, London School of Economics and Political Science, LSE Library.
    7. Turan G. Bali & Panayiotis Theodossiou, 2008. "Risk Measurement Performance of Alternative Distribution Functions," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 75(2), pages 411-437.
    8. Gaspar, Raquel M. & Schmidt, Thorsten, 2005. "Quadratic Portfolio Credit Risk models with Shot-noise Effects," Working Paper Series in Economics and Finance 616, Stockholm School of Economics.
    9. Rustam Ibragimov & Johan Walden, 2006. "The Limits of Diversification When Losses May Be Large," Harvard Institute of Economic Research Working Papers 2104, Harvard - Institute of Economic Research.
    10. Allen, Linda & Bali, Turan G., 2007. "Cyclicality in catastrophic and operational risk measurements," Journal of Banking & Finance, Elsevier, vol. 31(4), pages 1191-1235, April.
    11. Kabir Dutta & Jason Perry, 2006. "A tale of tails: an empirical analysis of loss distribution models for estimating operational risk capital," Working Papers 06-13, Federal Reserve Bank of Boston.
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    Cited by:
    1. Dahen, Hela & Dionne, Georges, 2010. "Scaling models for the severity and frequency of external operational loss data," Journal of Banking & Finance, Elsevier, vol. 34(7), pages 1484-1496, July.
    2. Chang, Carolyn W. & Chang, Jack S.K. & Lu, WeLi, 2010. "Pricing catastrophe options with stochastic claim arrival intensity in claim time," Journal of Banking & Finance, Elsevier, vol. 34(1), pages 24-32, January.
    3. Gillet, Roland & Hübner, Georges & Plunus, Séverine, 2010. "Operational risk and reputation in the financial industry," Journal of Banking & Finance, Elsevier, vol. 34(1), pages 224-235, January.
    4. Dominique Guegan & Bertrand K. Hassani, 2012. "Using a time series approach to correct serial correlation in Operational Risk capital calculation," Documents de travail du Centre d'Economie de la Sorbonne 12091r, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne, revised May 2013.
    5. Fiordelisi, Franco & Soana, Maria-Gaia & Schwizer, Paola, 2013. "The determinants of reputational risk in the banking sector," Journal of Banking & Finance, Elsevier, vol. 37(5), pages 1359-1371.
    6. Biell, Lis & Muller, Aline, 2013. "Sudden crash or long torture: The timing of market reactions to operational loss events," Journal of Banking & Finance, Elsevier, vol. 37(7), pages 2628-2638.
    7. Wang, Tawei & Hsu, Carol, 2013. "Board composition and operational risk events of financial institutions," Journal of Banking & Finance, Elsevier, vol. 37(6), pages 2042-2051.

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