Adding prior knowledge to quantitative operational risk models

Catalina Bolancé, Montserrat Guillén, Jim Gustafsson and Jens Perch Nielsen

Abstract

ABSTRACT

Our approach is based on the study of the statistical severity distribution of a single loss. We analyze the fundamental issues that arise in practice when modeling operational risk data. We address the statistical problem of estimating an operational risk distribution, both in situations where data is abundant and when our available data is challenged by the inclusion of external data or because of underreporting. Our paper includes an application to show that failure to account for underreporting may lead to a substantial underestimation of operational risk measures. The use of external data information can easily be incorporated into our modeling approach. The paper builds on methodology developed in a previous paper by the authors.