Robust estimation in operational risk modeling
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Over the last two decades, the modeling of operational risk has become increasingly important and it is nowadays mandatory for banks to allocate a capital charge to cover most large-scale operational losses. When standard estimation techniques are used, such as maximum likelihood, the estimated capital charge is highly sensitive to minor contamination of the operational loss data. This is a major issue an practice: large swings may be produced in the capital charge when a single or a few loss events are added to the database. We ensure stable capital charges by introducing the robust statistics framework, which is aimed at sacrificing some efficiency at the exact model, in order to gain robustness against minor deviations of the model. We show that using robust estimation techniques, the estimated capital charge maintains high efficiency at the exact model, while remaining stable under contamination of the operational loss data.