基于copula的追随者银行的企业项目总体风险评价模型

doi:10.16381/j.cnki.issn1003-207x.2015.01.013

中国管理科学 ›› 2015, Vol. 23 ›› Issue (1): 99-110.doi: 10.16381/j.cnki.issn1003-207x.2015.01.013

基于copula的追随者银行的企业项目总体风险评价模型

李战江^1,2, 迟国泰¹, 党均章³

1. 大连理工大学工商管理学院, 辽宁大连 116024;
2. 内蒙古农业大学理学院, 内蒙古呼和浩特 010018;
3. 中国邮政储蓄银行风险管理部, 北京 100001

收稿日期:2011-11-23 修回日期:2014-01-21 出版日期:2015-01-20 发布日期:2015-01-21
作者简介:李战江(1977-),男(汉族),内蒙古人,大连理工大学工商管理学院博士生,研究方向:银行风险管理决策理论与模型、复杂系统评价.
基金资助:
国家自然科学基金资助项目(71171031);教育部科学技术研究项目(2011-10);国家自然科学基金青年科学基金资助项目(71201018);教育部人文社会科学研究青年基金资助项目(11YJC790157);河北省自然科学基金青年科学基金(G2012501013);中国银监会银行业信息科技风险管理项目(2012-4-005);中国邮政储蓄银行总行小额贷款信用风险评价与贷款定价资助项目(2009-07);大连银行小企业信用风险评级系统与贷款定价项目(2012-01)

The Evaluation Model of Business Project Overall Risks of Follower Banks Based on Copula

LI Zhan-jiang^1,2, CHI Guo-tai¹, DANG Jun-zhang³

1. School of Business Management, Dalian University of Technology, Dalian 116024, China;
2. College of Science, Inner Mongolia Agricultural University, Huhhot 010018, China;
3. Department of Risk Management, Postal Savings Bank of China, Beijing 100001, China

Received:2011-11-23 Revised:2014-01-21 Online:2015-01-20 Published:2015-01-21

摘要/Abstract

摘要： 基于追随者银行的企业项目总体风险评价问题是指当有银行作为先行者介入一个项目时,后续的其它另一个银行作为追随者银行需要将先行者银行的信用风险和所要参与投资的企业项目风险综合加以考虑、从而独立判断项目投资的总体风险大小并进行投资决策。由于任何一家银行都只能熟悉某一些领域、某一些地区、某一些国家的项目,这就导致追随者银行在无法充分掌握项目信息时,需要以先行者银行的信用风险大小为参照物之一来推断企业项目的总体风险,这不仅仅对投资和贷款业务开展较晚的例如中国邮政储蓄银行这样的商业银行有着重要现实意义,而且对所有商业银行的投资活动都有重要的指导意义。通过先行者银行信用风险与项目风险反映企业项目总体风险,本研究建立了基于Copula函数的追随者银行的企业项目总体风险评价模型。本文主要的创新与特色是通过确定先行者银行的信用风险R_F与项目风险R_p的函数关系,进而确定企业项目总体风险R_T,解决了追随者银行所要测算企业项目风险的问题。总体风险模型的稳定性检验表明,在95%的置信水平下,对追随者银行来说,不论多大样本,其所要投资项目的总体风险中的先行者银行信用风险R_F与所要投资的项目风险R_p的重要程度分别为W₁=0.428、W₂=0.572。

关键词: 追随者银行, 先行者银行信用风险, 企业项目风险, 风险评价, Copula函数

Abstract: When a bank as forerunners joined a project, subsequent another bank as followers will comprehensively take the forerunner bank credit risk and the business project risk into consideration, and make investment decisions by the overall risk of investment projects. This is the evaluation question of business project overall risks based on follower banks. Because one bank is just familiar with the projects in some fields, some areas, some countries, which requirs follower banks need use the forerunner banks credit risk to infer the overall risk of business projects. According to the assumption that forerunner bank credit risk and project risk can reflect total project risk, the evaluation model of business project overall risks is established based on follower banks. The copula function is used to determine function relations of forerunner banks credit risk R_F and project risk R_P, total risk R_T is obtained, to solve measurement problem of business project overall risks at last. The result reveals that under 95% confidence levels, as far as follower banks are concerned, no matter sample numbers, important degree of forerunner bank credit risk R_F and project risk R_P is: W₁=0.428, W₂=0.572. This research has the great guide meaning not only for the follower banks of the later investment, but also for all inter-bank cooperation.

Key words: follower banks, forerunner banks credit risk, business project risk, risk evaluation, Copula function

中图分类号:

李战江, 迟国泰, 党均章. 基于copula的追随者银行的企业项目总体风险评价模型[J]. 中国管理科学, 2015, 23(1): 99-110.

LI Zhan-jiang, CHI Guo-tai, DANG Jun-zhang. The Evaluation Model of Business Project Overall Risks of Follower Banks Based on Copula[J]. Chinese Journal of Management Science, 2015, 23(1): 99-110.

参考文献

[1] García V, Marqués A I, Sánchez J S. On the use of data filtering techniques for credit risk prediction with instance-based models [J]. Expert Systems with Applications, 2012, 39(18): 13267-13276.

[2] Baourakis G, Conisescu M, Dijk G V, et al. A multicriteria approach for rating the credit risk of financial institutions [J].Computational Management Science, 2009, 6(3): 347-356.

[3] 王国栋,詹原瑞.信用风险中回收率分布的双Beta模型[J].中国管理科学,2011,19(6):10-14.

[4] 李建平,丰吉闯,宋浩等.风险相关性下的信用风险、市场风险和操作风险集成度量[J].中国管理科学,2010,18(1):18-25.

[5] Danenas P, Garsva G. Credit risk evaluation modeling using evolutionary linear SVM classifiers and sliding window approach [J]. Procedia Computer Science, 2012, 9: 1324-1333.

[6] Bahrammirzaee A, Ghatari A R, Ahmadi P, et al. Hybrid credit ranking intelligent system using expert system and artificial neural networks [J]. Appl Intell,2011,34(1):28-46.

[7] 徐超,杨扬,周宗放.基于元胞自动机的企业集团信用风险传递仿真[J].中国管理科学,2012,20(4):144-150.

[8] Moody's. Rating methodology for project finance transactions [R]. New York, Moody's, 2003.

[9] Stand & Poor's. Global project finance year book [R]. New York, Stand & Poor's, 2008.

[10] Komendantova N, Patt A, Barras L, et al. Perception of risks in renewable energy projects: the case of concentrated solar power in North Africa [J]. Energy Policy, 2012, 40: 103-109.

[11] 林则夫.基于巴塞尔新资本协议的项目融资贷款风险评价体系[J].中国管理科学,16(S1): 346-352.

[12] Buntaine M T. Does the asian development bank respond to past environmental performance when allocating environmentally risky financing?[J]. World Development, 2011, 39(3): 336-350.

[13] Chen Wenjun, Li Cunbin, Yang Yang, et al. Transmission model of risk breakdown structure in engineering project-chain based on entropy risk element [J]. Systems Engineering Procedia, 2012, 4: 268-274.

[14] Chao Fang, Marle F. A simulation-based risk network model for decision support in project risk management [J]. Decision Support Systems, 2012, 52(3): 635-644.

[15] Luo Fuzhou, Gao Hongyuan. The risk assessment model of BT construction engineering project financing [J]. Systems Engineering Procedia, 2011, 1: 169-173.

[16] Idrus A, Nuruddin M F, Rohman M A. Development of project cost contingency estimation model using risk analysis and fuzzy expert system [J]. Expert Systems with Applications, 2011, 38(3): 1501-1508.

[17] 谷晓燕,何锋,蔡晨.风险条件下基于实物期权的研发项目多阶段评价模型[J].中国管理科学,2011,19(4):68-75.

[18] 大连理工大学迟国泰课题组.银行间交易对手债项信用风险管理系统[R].大连理工大学,2010.

[19] 中国证券业协会.信用评级对中国资本市场的重要意义[EB/OL]. http://www.sac.net.cn/. 2010.

[20] 郭亚军.综合评价理论、方法及应用[M].北京:科学出版社, 2002.

[21] 韦艳华,张世英.Copula理论及其在金融分析上的应用[M].北京:清华大学出版社,2008.

[22] 马志卫,刘应宗.基于蒙特卡洛模拟的贷款组合优化决策方法[J].管理科学,2006,19(3):66-70.

[23] Bombay R B.Distribution of Chi-Square analogue for normal population with class intervals defined in terms of the sample quartiles [J].Metrika,1973,20(1):203-208.

[24] 人民网.银团贷款项目[EB/OL]. (2010-03-10).http://www. people. com.cn, 2010-03-10.

[25] 网易金融频道.银团贷款项目[EB/OL]. (2010-03-10). http://www. money.163.com/finance/.

基于copula的追随者银行的企业项目总体风险评价模型

The Evaluation Model of Business Project Overall Risks of Follower Banks Based on Copula

PDF (PC)

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 8

Metrics

本文评价

推荐阅读 10

[1]	董路安, 叶鑫. 基于改进教学式方法的可解释信用风险评价模型构建[J]. 中国管理科学, 2020, 28(9): 45-53.
[2]	柴尚蕾, 周鹏. 基于非参数Copula-CVaR模型的碳金融市场集成风险测度[J]. 中国管理科学, 2019, 27(8): 1-13.
[3]	程砚秋, 徐占东. 基于泰尔指数修正的ELECTRE III小企业信用评价模型[J]. 中国管理科学, 2019, 27(10): 22-33.
[4]	李永, 胡帅, 范蓓. 随机利率下跨期多事件触发巨灾债券定价研究[J]. 中国管理科学, 2013, 21(5): 8-14.
[5]	陆静, 张佳. 基于极值理论和多元Copula函数的商业银行操作风险计量研究[J]. 中国管理科学, 2013, 21(3): 11-19.
[6]	戴晓凤, 梁巨方. 基于时变Copula函数的下偏矩最优套期保值效率测度方法研究[J]. 中国管理科学, 2010, 18(6): 26-32.
[7]	李健伦, 方兆本. 估算我国保监会对产险业的容许破产概率[J]. 中国管理科学, 2006, (4): 6-12.
[8]	徐宪平. 风险投资的风险评价与控制[J]. 中国管理科学, 2001, (4): 75-80.