Abstract
In this paper three information criteria are employed to assess the truncated operational risk models. The performances of the three information criteria on distinguishing the models are compared. The competing models are constructed using Champernowne, Frechet, Lognormal, Lomax, Paralogistic, and Weibull distributions, respectively. Simulation studies are conducted before a case study. In the case study, certain distributional models conform to the external fraud type of risk data in retail banking of Chinese banks. However, those models are difficult to distinguish using standard information criteria such as Akaike Information Criterion and Bayesian Information Criterion. We have found no single information criterion is absolutely more effective than others in the simulation studies. But the information complexity based ICOMP criterion says a little bit more if AIC and/or BIC cannot kick the Lognormal model out of the pool of competing models.
Highlights
This paper mainly applies model selection information criteria to operational risk models subject to data truncation
We can see there is a difference of no
We have studied the problem of model selection in operational risk modeling, which arises due to that various truncated models are all validated but cannot be distinguished
Summary
This paper mainly applies model selection information criteria to operational risk models subject to data truncation. The truncated models, compared to the shifted models and naive models, have been determined to be appropriate to model loss data for operational risk [1]. Model selection criteria are further used to separate a most suitable model from the rest. Traditional information criteria such as Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) have been documented [2] for a banking organization to employ when comparing alternative models. Using AIC and/or BIC, they have determined the overall best distributional model(s) for internal data or external data for operational risk in financial institutions
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