A "Toy" Model for Operational Risk Quantification using Credibility Theory

TL;DR

This paper introduces a credibility theory-based model for operational risk quantification, integrating internal data, external data, and expert opinions to meet Basel II requirements for high-impact, low-frequency losses.

Contribution

It proposes a novel credibility theory approach to combine diverse data sources for operational risk modeling, addressing a key challenge in regulatory compliance.

Findings

Effective estimation of loss frequency and severity distributions.

Improved risk quantification by integrating multiple data sources.

Potential for enhanced regulatory compliance and risk management.

Abstract

To meet the Basel II regulatory requirements for the Advanced Measurement Approaches in operational risk, the bank's internal model should make use of the internal data, relevant external data, scenario analysis and factors reflecting the business environment and internal control systems. One of the unresolved challenges in operational risk is combining of these data sources appropriately. In this paper we focus on quantification of the low frequency high impact losses exceeding some high threshold. We suggest a full credibility theory approach to estimate frequency and severity distributions of these losses by taking into account bank internal data, expert opinions and industry data.