Quantifying operational risk guided by kernel smoothing and continuous credibility: A practitioner's view

Jim Gustafsson, Jens Perch Nielsen, Paul Pritchard, Dix Roberts

Abstract

ABSTRACT

This paper considers the benefits of applying sophisticated statistical techniques to challenges faced in the quantification of operational risk. The evolutionary nature of operational risk modeling to establish capital charges is recognized, emphasizing the importance of capturing tail behavior. Non-parametric smoothing techniques are considered along with a parametric base with a particular view to comparison with extreme value theory. This is presented without detailed proofs with the aim of demonstrating to practitioners the practical benefits of such techniques. The smoothed estimators embedded in a credibility approach supports analysis from pooled data across lines of business or across risk types/regions.