Abstract
Not all claims are reported when a financial operational risk data base is created. The probability of reporting increase with the size of the operational risk loss and approaches one for very big losses. Operational risk losses comes from many different sources and can be expected to follow a wide variety of distributional shapes. Therefore, an approach to operational risk modelling based on one or two favourites of parametric models are deemed to fail. In this paper we introduce a semiparametric approach to operational risk modelling that is able to take underreporting into account and that allows itself to be guided by prior knowledge of distributional shape.
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