Implementing Loss Distribution Approach for Operational Risk

TL;DR

This paper reviews the implementation of the Loss Distribution Approach for operational risk under Basel II, focusing on Bayesian methods, dependence modeling, and insurance inclusion to improve risk quantification.

Contribution

It provides a comprehensive review of quantitative methods, especially Bayesian inference, for practical implementation of the Loss Distribution Approach in operational risk management.

Findings

Bayesian inference effectively incorporates expert judgment and parameter uncertainty.

Modeling dependence improves the accuracy of risk estimates.

Including insurance impacts the loss distribution and capital calculation.

Abstract

To quantify the operational risk capital charge under the current regulatory framework for banking supervision, referred to as Basel II, many banks adopt the Loss Distribution Approach. There are many modeling issues that should be resolved to use the approach in practice. In this paper we review the quantitative methods suggested in literature for implementation of the approach. In particular, the use of the Bayesian inference method that allows to take expert judgement and parameter uncertainty into account, modeling dependence and inclusion of insurance are discussed.