On the Selection of Loss Severity Distributions to Model Operational Risk

TL;DR

This paper discusses methods for selecting stable and accurate loss severity distributions in operational risk modeling, emphasizing truncation probability estimates, a new flexible distribution family, and the importance of collecting censored data.

Contribution

It introduces truncation probability estimates and a scoring function for better severity distribution selection, proposes the Sinh-arcSinh distribution as a flexible modeling option, and advocates for collecting censored loss data.

Findings

Truncation probability estimates improve distribution stability.

Sinh-arcSinh distribution offers flexible modeling of loss severities.

Collecting censored data enhances model accuracy.

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.