The Quantification of Operational Risk using Internal Data, Relevant External Data and Expert Opinions

TL;DR

This paper introduces a Bayesian inference method to combine internal data, external data, and expert opinions for more accurate operational risk quantification under Basel II.

Contribution

A novel Bayesian approach that integrates multiple data sources for estimating operational risk parameters, improving upon traditional methods.

Findings

Enhanced accuracy in risk parameter estimation.

Effective integration of diverse data sources.

Potential for better capital charge calculations.

Abstract

To quantify an operational risk capital charge under Basel II, many banks adopt a Loss Distribution Approach. Under this approach, quantification of the frequency and severity distributions of operational risk involves the bank's internal data, expert opinions and relevant external data. In this paper we suggest a new approach, based on a Bayesian inference method, that allows for a combination of these three sources of information to estimate the parameters of the risk frequency and severity distributions.