Empirically assessing and modeling spillover effects from operational risk events in the insurance industry

Abstract

The aim of this paper is to propose the first mathematical model for spillover effects caused by operational losses and to calibrate it based on an extensive empirical study of spillover effects and their influencing factors in the US and European banking and insurance industry. Our event study shows significant spillover effects due to operational losses, whereby a higher number of firms faces contagion effects than competitive effects. A regression analysis further reveals that spillover effects are rather information-based than pure, as event and firm characteristics have a significant impact, specifically external fraud, the return on equity of the announcing firm and the similarity between the announcing and the non-announcing firm in terms of size. Based on the empirical findings, we fit a distribution and model spillover effects and underlying operational losses to assess respective risk measures by means of a simulation analysis. The results show that spillover risk can be considerable for non-announcing firms as well as from a portfolio view, which has important risk management implications.