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Discrete Dynamics in Nature and Society
Volume 2014 (2014), Article ID 839731, 8 pages
Research Article

A Nonparametric Operational Risk Modeling Approach Based on Cornish-Fisher Expansion

1Institution of Policy and Management, Chinese Academy of Sciences, Beijing 100190, China
2University of Chinese Academy of Sciences, Beijing 100190, China
3Business School, University of Hull, Hull Hu6 7RX, UK
4School of Business Administration, Shandong University of Finance and Economics, Jinan, Shandong 250014, China
5Industrial Bank CO., Ltd., Fuzhou, Fujian 350003, China

Received 7 February 2014; Accepted 11 March 2014; Published 31 March 2014

Academic Editor: Fenghua Wen

Copyright © 2014 Xiaoqian Zhu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.