Applying robust methods to operational risk modeling

ABSTRACT We use robust statistical methods to analyze operational loss data. Commonly used classical estimators of model parameters may be sub-optimal under minor departures of data from the model assumptions. Operational loss data are characterized by a very heavy right tail of the loss distribution attributed to several “low frequency/high severity” events. Classical estimators may produce biased estimates of parameters leading to unreasonably high estimates of mean, variance and the operational risk VAR and CVAR measures. The main objective of robust methods is to focus the analysis on the fundamental properties of the bulk of the data, without being distorted by outliers. We argue that further comparison of results obtained under the classical and robust procedures can serve as a basis for the VAR sensitivity analysis and can lead to an understanding of the economic role played by these extreme events. An empirical study with 1980–2002 public operational loss data reveals that the highest 5% of losses account for up to 76% of the operational risk capital charge.