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The dynamics of operational loss clustering

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  • Chernobai, Anna
  • Yildirim, Yildiray

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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  • Chernobai, Anna & Yildirim, Yildiray, 2008. "The dynamics of operational loss clustering," Journal of Banking & Finance, Elsevier, vol. 32(12), pages 2655-2666, December.
    • Handle: RePEc:eee:jbfina:v:32:y:2008:i:12:p:2655-2666
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    Cited by:

    1. Xingnan Jiang, 2018. "Operational risk and its impact on North American and British banks," Applied Economics, Taylor & Francis Journals, vol. 50(8), pages 920-933, February.
    2. 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.
    3. Al-Amri, Khalid & Davydov, Yevgeniy, 2016. "Testing the effectiveness of ERM: Evidence from operational losses," Journal of Economics and Business, Elsevier, vol. 87(C), pages 70-82.
    4. 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.
    5. 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.
    6. 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.
    7. Roc'io Paredes & Marco Vega, 2020. "An internal fraud model for operational losses in retail banking," Papers 2002.03235, arXiv.org.
    8. Dionne, Georges & Saissi-Hassani, Samir, 2016. "Hidden Markov Regimes in Operational Loss Data: Application to the Recent Financial Crisis," Working Papers 15-3, HEC Montreal, Canada Research Chair in Risk Management.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. Xu, Chi & Zheng, Chunling & Wang, Donghua & Ji, Jingru & Wang, Nuan, 2019. "Double correlation model for operational risk: Evidence from Chinese commercial banks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 327-339.

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