On the Selection of Loss Severity Distributions to Model Operational Risk
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 yeartoyear 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 SinharcSinh 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.
 Publication:

arXiv eprints
 Pub Date:
 July 2021
 arXiv:
 arXiv:2107.03979
 Bibcode:
 2021arXiv210703979H
 Keywords:

 Quantitative Finance  Risk Management
 EPrint:
 Submitted to Journal of Operational Risk on October 19, 2018