Comparing Model Selection Criteria to Distinguish Truncated Operational Risk Models
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 W...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2020-08-01
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| Series: | Frontiers in Applied Mathematics and Statistics |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fams.2020.00028/full |
| _version_ | 1818085004561874944 |
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| author | Daoping Yu |
| author_facet | Daoping Yu |
| author_sort | Daoping Yu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-10T20:02:53Z |
| format | Article |
| id | doaj.art-d2fe6bb77fea4f2981b111daec4b2e1b |
| institution | Directory Open Access Journal |
| issn | 2297-4687 |
| language | English |
| last_indexed | 2024-12-10T20:02:53Z |
| publishDate | 2020-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Applied Mathematics and Statistics |
| spelling | doaj.art-d2fe6bb77fea4f2981b111daec4b2e1b2022-12-22T01:35:28ZengFrontiers Media S.A.Frontiers in Applied Mathematics and Statistics2297-46872020-08-01610.3389/fams.2020.00028557971Comparing Model Selection Criteria to Distinguish Truncated Operational Risk ModelsDaoping YuIn 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.https://www.frontiersin.org/article/10.3389/fams.2020.00028/fullinformation criteriamodel selectionoperational risktruncated modelsValue at Risk (VaR) |
| spellingShingle | Daoping Yu Comparing Model Selection Criteria to Distinguish Truncated Operational Risk Models Frontiers in Applied Mathematics and Statistics information criteria model selection operational risk truncated models Value at Risk (VaR) |
| title | Comparing Model Selection Criteria to Distinguish Truncated Operational Risk Models |
| title_full | Comparing Model Selection Criteria to Distinguish Truncated Operational Risk Models |
| title_fullStr | Comparing Model Selection Criteria to Distinguish Truncated Operational Risk Models |
| title_full_unstemmed | Comparing Model Selection Criteria to Distinguish Truncated Operational Risk Models |
| title_short | Comparing Model Selection Criteria to Distinguish Truncated Operational Risk Models |
| title_sort | comparing model selection criteria to distinguish truncated operational risk models |
| topic | information criteria model selection operational risk truncated models Value at Risk (VaR) |
| url | https://www.frontiersin.org/article/10.3389/fams.2020.00028/full |
| work_keys_str_mv | AT daopingyu comparingmodelselectioncriteriatodistinguishtruncatedoperationalriskmodels |