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On the Selection of Loss Severity Distributions to Model Operational Risk

Author

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  • Daniel Hadley
  • Harry Joe
  • Natalia Nolde

Abstract

Accurate modeling of operational risk is important for a bank and the finance industry as a whole to prepare for potentially catastrophic losses. One approach to modeling operational is the loss distribution approach, which requires a bank to group operational losses into risk categories and select a loss frequency and severity distribution for each category. This approach estimates the annual operational loss distribution, and a bank must set aside capital, called regulatory capital, equal to the 0.999 quantile of this estimated distribution. In practice, this approach may produce unstable regulatory capital calculations from year-to-year as selected loss severity distribution families change. This paper presents truncation probability estimates for loss severity data and a consistent quantile scoring function on annual loss data as useful severity distribution selection criteria that may lead to more stable regulatory capital. Additionally, the Sinh-arcSinh distribution is another flexible candidate family for modeling loss severities that can be easily estimated using the maximum likelihood approach. Finally, we recommend that loss frequencies below the minimum reporting threshold be collected so that loss severity data can be treated as censored data.

Suggested Citation

  • Daniel Hadley & Harry Joe & Natalia Nolde, 2021. "On the Selection of Loss Severity Distributions to Model Operational Risk," Papers 2107.03979, arXiv.org.
  • Handle: RePEc:arx:papers:2107.03979
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    References listed on IDEAS

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    1. Gneiting, Tilmann, 2011. "Making and Evaluating Point Forecasts," Journal of the American Statistical Association, American Statistical Association, vol. 106(494), pages 746-762.
    2. Degen, Matthias & Embrechts, Paul & Lambrigger, Dominik D., 2007. "The Quantitative Modeling of Operational Risk: Between G-and-H and EVT," ASTIN Bulletin, Cambridge University Press, vol. 37(2), pages 265-291, November.
    3. M. C. Jones & Arthur Pewsey, 2009. "Sinh-arcsinh distributions," Biometrika, Biometrika Trust, vol. 96(4), pages 761-780.
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