Predicting Operational Loss Exposure Using Past Losses

Abstract

Operational risk models, such as the loss distribution approach, frequently use past internal losses to forecast operational loss exposure. However, the ability of past losses to predict exposure, particularly tail exposure, has not been thoroughly examined in the literature. In this paper, we test whether simple metrics derived from past loss experience are predictive of future tail operational loss exposure using quantile regression. We find evidence that past losses are predictive of future exposure, particularly metrics related to loss frequency.

Keywords: Banking Regulation, Risk Management, Operational Risk, Tail Risk, Quantile Regression

JEL Classification: G21, G28, G32