Estimation of Operational Risk Capital Charge under Parameter Uncertainty

TL;DR

This paper introduces a Bayesian framework to incorporate parameter uncertainty into operational risk capital estimation, improving accuracy by including expert opinions and external data, addressing a common oversight in Basel II compliance.

Contribution

It presents a novel Bayesian approach for quantifying parameter uncertainty in operational risk capital calculation, enhancing existing methods by integrating additional information sources.

Findings

Parameter uncertainty significantly impacts capital estimates.

Bayesian methods effectively incorporate expert opinions and external data.

The approach provides more robust and comprehensive risk capital assessments.

Abstract

Many banks adopt the Loss Distribution Approach to quantify the operational risk capital charge under Basel II requirements. It is common practice to estimate the capital charge using the 0.999 quantile of the annual loss distribution, calculated using point estimators of the frequency and severity distribution parameters. The uncertainty of the parameter estimates is typically ignored. One of the unpleasant consequences for the banks accounting for parameter uncertainty is an increase in the capital requirement. This paper demonstrates how the parameter uncertainty can be taken into account using a Bayesian framework that also allows for incorporation of expert opinions and external data into the estimation procedure.