Combining Underreported Internal and External Data for Operational Risk Measurement

Operational risk data sets have two types of sample selection problems: truncation below a given threshold due to data that are not recorded and random censoring above that level caused by data that are not reported. This paper proposes a model for operational losses that improves the internal loss distribution modelling by combining internal and external operational risk data. It also considers the possibility that internal and externa l data have been collected with a different truncation threshold. Moreover, the model is able to cope with unreported losses by means of an estimated underreporting function.

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