期刊文献+

操作风险整体评估方法:基于拓扑数据模型的影响图 被引量:3

Assessment of operational risk:An influence diagrams approach based on topological data model
原文传递
导出
摘要 考虑到一般操作风险数据模型的不足,提出了一种新的数据模型——操作风险拓扑数据模型.它能够包含风险事件发生过程中各种风险原因之间相互关系的信息,且能支持操作风险的影响图度量方法.随后采用基于拓扑数据模型的影响图方法对操作风险整体评估进行了研究.该方法能够辅助分析各个风险原因之间的相关性,并支持基于拓扑数据模型的操作风险数据的统计分析.最后以一家保险企业为例演示了该方法.基于拓扑数据模型的操作风险影像图度量方法针对操作风险特点,关注控制和流程的失败,更充分利用历史事件中蕴含的信息,简化了各风险原因之间的相关性分析. Considering the shortage of the general model -- operational risk topological data model operational risk data model, a new operational risk data is brought forward, which can contain the information about the connection between all risk causes as the risk event was going on, and support the operational risk measurement by influence diagrams approach. Then the integrated analyses and assessment of operational risk are studied by an influence diagrams approach based on topological data model. Operational risk influence diagrams can assist the analysis of correlation between all the causes, and support the statistic analysis to operational risk topological data. An insurance company was taken as the object in case study for illustration at last. The new operational risk measurement approach focus on failures of control and process, uses the information contained in the operational risk historical event more effectively and can simplify the correlation analysis of risk causes.
作者 赵蕾 张庆洪
出处 《系统工程理论与实践》 EI CSSCI CSCD 北大核心 2010年第9期1563-1571,共9页 Systems Engineering-Theory & Practice
基金 上海高校选拔培养优秀青年教师科研专项基金(SWM-07003) 上海市教委重点学科金融学建设项目(J512-01)
关键词 操作风险 度量 数据模型 整体评估 相关性 operational risk measurement data model integrated assessment correlation
  • 相关文献

参考文献20

  • 1Giudici P. Integration of Qualitative and Quantitative Operational Risk Data: A Bayesian Approach[M]. Operational Risk Modelling and Analysis, Risk Books, 2004.
  • 2Neil M, Fenton N, Tailor M. Using bayesian networks to model expected and unexpected operational losses[J]. Risk Analysis, 2005, 25(4): 963 972.
  • 3叶永刚,曲锴.商业银行操作风险度量和管理的贝叶斯方法[J].生产力研究,2008(4):34-37. 被引量:4
  • 4The Operational Risks Working Party. Report of the operational risks working party to GIRO[R]. www.louisepry or.com/papers/giro02-oprisk.pdf, 2002.
  • 5Serwer J. Build and Operate Operational Loss Data[M]. Advances in Operational Risk: Firm-wide Issues for Financial Institutions, Risk Waters Group, 2001.
  • 6King J L. Operational Risk: Measurement and Modelling[M]. John Wiley & Sons, 2001.
  • 7Doerig H U. Operational Risk in Financial Services[M]. Credit Suisse Group, 2003.
  • 8赵蕾,张庆洪.我国保险企业操作风险特点评析[J].上海保险,2006(5):6-9. 被引量:6
  • 9Alexander C. Using Bayesian Network to Manage Operational Risk Operational Risk[M]. Pearson Education Limited, 2003.
  • 10郭仲伟.风险分析与决策[M].北京:机械工业出版社,1986..

二级参考文献2

共引文献39

同被引文献34

  • 1温树海.贝叶斯网络模型在商业银行操作风险管理中的应用[J].商场现代化,2005(12X):395-395. 被引量:4
  • 2刘家鹏,詹原瑞,刘睿.基于贝叶斯网络的操作风险建模[J].西安电子科技大学学报(社会科学版),2007,17(4):32-39. 被引量:7
  • 3Ferro C, Segers J. Inference for clusters of extreme values [J]. Journal of the Royal Statistical Society, 2003, 65(2).
  • 4V Chavez--Demoulin, P Embrechts, J NellehovA. Quantitative Models for Operational Risk: Extremes, Dependenceand Aggregation [J]. Journal of Banking and Finance, 2006, 30(10).
  • 5Cornalba C, Giudiei P. Statistical Models for Operational Risk Management[J]. Physica A, 2004,338(7).
  • 6Cummins J, David Christopher, M. Lewis, Wei Ran. Market value impact of operational loss events for US bank and in-surers[J]. Journal of Banking and Finance, 2006,30(10).
  • 7Hans BysrE. Managing Extreme Risks in Tranquil and Volatile Markets Using Conditional Extreme Value Theory[J]. International Review of Financial Analysis, 2004,13 (2).
  • 8Stelios Bekiros D, Dimitris Georgoutsos A. Estimation of Value--at--Risk by Extreme Value and Conventional Meth-ods: a Comparative Evaluation of Their Predictive Performance[J]. International Financial Markets, Institutions andMoney, 2005(7).
  • 9Brooks C, Clare A D, Dalle Molle J W, Persand G. A Comparison of Extreme Value Theory Approaches for EeterminingValue at Risk [J]. Empirical Finance, 2005(3).
  • 10S Resnick, C starica. Smoothing the Hill Estimator[J]. Advance in Applied Probability, 1997,29(3).

二级引证文献4

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部