A Nonparametric Operational Risk Modeling Approach Based on Cornish-Fisher Expansion
It is generally accepted that the choice of severity distribution in loss distribution approach has a significant effect on the operational risk capital estimation. However, the usually used parametric approaches with predefined distribution assumption might be not able to fit the severity distribut...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2014/839731 |
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| author | Xiaoqian Zhu Jianping Li Jianming Chen Yingqi YangHuo Lijun Gao Jichuang Feng Dengsheng Wu Yongjia Xie |
| author_facet | Xiaoqian Zhu Jianping Li Jianming Chen Yingqi YangHuo Lijun Gao Jichuang Feng Dengsheng Wu Yongjia Xie |
| author_sort | Xiaoqian Zhu |
| collection | DOAJ |
| description | It is generally accepted that the choice of severity distribution in loss distribution approach has a significant effect on the operational risk capital estimation. However, the usually used parametric approaches with predefined distribution assumption might be not able to fit the severity distribution accurately. The objective of this paper is to propose a nonparametric operational risk modeling approach based on Cornish-Fisher expansion. In this approach, the samples of severity are generated by Cornish-Fisher expansion and then used in the Monte Carlo simulation to sketch the annual operational loss distribution. In the experiment, the proposed approach is employed to calculate the operational risk capital charge for the overall Chinese banking. The experiment dataset is the most comprehensive operational risk dataset in China as far as we know. The results show that the proposed approach is able to use the information of high order moments and might be more effective and stable than the usually used parametric approach. |
| format | Article |
| id | doaj-art-3e30f07866d9413db20b4923b699e9a2 |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-3e30f07866d9413db20b4923b699e9a22025-02-03T01:02:28ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2014-01-01201410.1155/2014/839731839731A Nonparametric Operational Risk Modeling Approach Based on Cornish-Fisher ExpansionXiaoqian Zhu0Jianping Li1Jianming Chen2Yingqi YangHuo3Lijun Gao4Jichuang Feng5Dengsheng Wu6Yongjia Xie7Institution of Policy and Management, Chinese Academy of Sciences, Beijing 100190, ChinaInstitution of Policy and Management, Chinese Academy of Sciences, Beijing 100190, ChinaInstitution of Policy and Management, Chinese Academy of Sciences, Beijing 100190, ChinaBusiness School, University of Hull, Hull Hu6 7RX, UKSchool of Business Administration, Shandong University of Finance and Economics, Jinan, Shandong 250014, ChinaIndustrial Bank CO., Ltd., Fuzhou, Fujian 350003, ChinaInstitution of Policy and Management, Chinese Academy of Sciences, Beijing 100190, ChinaInstitution of Policy and Management, Chinese Academy of Sciences, Beijing 100190, ChinaIt is generally accepted that the choice of severity distribution in loss distribution approach has a significant effect on the operational risk capital estimation. However, the usually used parametric approaches with predefined distribution assumption might be not able to fit the severity distribution accurately. The objective of this paper is to propose a nonparametric operational risk modeling approach based on Cornish-Fisher expansion. In this approach, the samples of severity are generated by Cornish-Fisher expansion and then used in the Monte Carlo simulation to sketch the annual operational loss distribution. In the experiment, the proposed approach is employed to calculate the operational risk capital charge for the overall Chinese banking. The experiment dataset is the most comprehensive operational risk dataset in China as far as we know. The results show that the proposed approach is able to use the information of high order moments and might be more effective and stable than the usually used parametric approach.http://dx.doi.org/10.1155/2014/839731 |
| spellingShingle | Xiaoqian Zhu Jianping Li Jianming Chen Yingqi YangHuo Lijun Gao Jichuang Feng Dengsheng Wu Yongjia Xie A Nonparametric Operational Risk Modeling Approach Based on Cornish-Fisher Expansion Discrete Dynamics in Nature and Society |
| title | A Nonparametric Operational Risk Modeling Approach Based on Cornish-Fisher Expansion |
| title_full | A Nonparametric Operational Risk Modeling Approach Based on Cornish-Fisher Expansion |
| title_fullStr | A Nonparametric Operational Risk Modeling Approach Based on Cornish-Fisher Expansion |
| title_full_unstemmed | A Nonparametric Operational Risk Modeling Approach Based on Cornish-Fisher Expansion |
| title_short | A Nonparametric Operational Risk Modeling Approach Based on Cornish-Fisher Expansion |
| title_sort | nonparametric operational risk modeling approach based on cornish fisher expansion |
| url | http://dx.doi.org/10.1155/2014/839731 |
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