Abstract
In this presentation, the effect of data truncation on model stability is shown to be significant due to implied extrapolation into the region below threshold. Since the instability results from the absence of data below the truncation point, it can't be addressed by alternative fitting techniques. New information needs to be leveraged to resolve the problem. One source of information is the aggregated data of operational risk losses that fall below the truncation point, data which is often available but not used in modeling. The presentation demonstrates how to utilize this data to improve estimates of model distribution parameters and capital.
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