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Abstract: We consider three model selection information criteria to assess the operational risk models. They are Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and Information Complexity (ICOMP) based Criterion. We compare the performances of the three information criteria on distinguishing the operational risk severity models. The competing models are constructed using Champernowne, Frechet, Lognormal, Lomax, Paralogistic, and Weibull distributions, respectively. We conduct both simulation studies and a case study. It seems that no single information criterion is absolutely more effective than others in the simulation studies. In the case study, certain distributional models conform to the external fraud type of operational losses data in retail banking of Chinese banks. However, those models are difficult to distinguish using standard information criteria such as AIC and BIC. We have found the ICOMP criterion conveys a little bit more information when AIC and/or BIC cannot separate the Lognormal model out of the pool of competing models.
About: Daoping Yu received his Ph.D. degree in Mathematics with concentration in Statistics and Actuarial Science after obtaining his M.S. degree in Mathematics and B.S. degree in Information Management and Information Systems. He joined the School of Computer science and Mathematics at the University of Central Missouri in 2016. He has taught a variety of courses in mathematics, statistics and actuarial science. The courses include but are not limited to Statistical Modeling (STAM), Life Contingencies Models (LTAM), Mathematical Statistics (VEE), Review for Exams Probability (P) and Financial Mathematics (FM), Calculus, and Basic Statistics. His research is in the interface of actuarial science and financial mathematics with particular emphasis on the statistical modeling of operational risk management. He is an Associate of the Society of Actuaries (ASA).
