基于熵空间交互理论的CRT网络信用风险传染模型

doi:10.16381/j.cnki.issn1003-207x.2016.06.002

中国管理科学 ›› 2016, Vol. 24 ›› Issue (6): 10-18.doi: 10.16381/j.cnki.issn1003-207x.2016.06.002

基于熵空间交互理论的CRT网络信用风险传染模型

陈庭强^1,2, 李心丹², 何建敏³

1. 南京工业大学经济与管理学院, 江苏南京 211816;
2. 南京大学工程管理学院, 江苏南京 210093;
3. 东南大学经济管理学院, 江苏南京 211189

收稿日期:2013-06-14 修回日期:2014-01-20 出版日期:2016-06-20 发布日期:2016-07-05
通讯作者: 陈庭强(1983-),男(汉族),河南信阳人,南京大学工程管理学院博士后,南京工业大学经济与管理学院讲师,研究方向:金融工程与风险管理,E-mail:tingqiang88888888@163.com. E-mail:tingqiang88888888@163.com
基金资助:
国家自然科学基金资助项目(71501094);江苏省自然科学基金资助项目(BK20150961);江苏省高校自然科学研究面上项目(15KJB120003);中国博士后科学基金面上资助项目(2014M561626);江苏高校哲学社会科学研究基金资助项目(2014SJB081)

Spatial Interaction Theory-based Credit Risk Contagion Model for the CRT Network

CHEN Ting-qiang^1,2, LI Xin-dan², HE Jian-min³

1. School of Economics and Management, Nanjing Tech University, Nanjing 211816, China;
2. School of Management and Engineering, Nanjing University, Nanjing 210093, China;
3. School of Economics and Management, Southeast University, Nanjing 211189, China

Received:2013-06-14 Revised:2014-01-20 Online:2016-06-20 Published:2016-07-05

摘要/Abstract

摘要： 信用风险转移(Credit Risk Transfer,CRT)网络中信用风险传染已逐渐成为学术界和政策制定者关注的热点。本文基于熵空间交互理论,将CRT网络中银行和投资者之间空间距离与非线性耦合、银行的信用风险转移能力与投资者的风险偏好相结合,建立了CRT网络信用风险传染的熵空间模型。通过数值模拟和对参数的敏感性分析发现,模型可以很好地反映银行和投资者之间的空间距离与非线性耦合、银行的信用风险转移能力、信用风险在投资者节点上的集中程度、投资者的风险偏好和风险承受能力对CRT网络信用风险传染效应的影响机制。研究同时发现:CRT网络信用风险传染具有"本地效应"和"关联抑制效应"。

关键词: 信用风险传染, 熵空间模型, CRT网络, 空间交互理论

Abstract: Based on the entropy spatial interaction theory, an entropy spatial model of credit risk contagion that combines with the spatial distance and nonlinear coupling between the banks and the investors, the transfer ability of credit risk of the banks, and the appetite for risk of investors is build. By means of numerical simulation and the sensitivity analysis, it is found that the model can well describe the effect of the spatial distance and nonlinear coupling between the banks and the investors, the transfer ability of credit risk of the banks, the concentration of credit risk of investor, the appetite for risk of investors, and the risk capacity on credit risk contagion in the CRT network. In addition, it is also indicated that credit risk contagion of the CRT network has "home market effects" and "correlation inhibition effects".

Key words: credit risk contagion, entropy spatial model, CRT network, spatial interaction theory

中图分类号:

陈庭强, 李心丹, 何建敏. 基于熵空间交互理论的CRT网络信用风险传染模型[J]. 中国管理科学, 2016, 24(6): 10-18.

CHEN Ting-qiang, LI Xin-dan, HE Jian-min. Spatial Interaction Theory-based Credit Risk Contagion Model for the CRT Network[J]. Chinese Journal of Management Science, 2016, 24(6): 10-18.

参考文献

[1] Davis M, Lo V. Modelling default correlation in bond portfolios[R]. Working Paper, Imperial College, London,2000.

[2] Davis M, Lo V. Infectious defaults[J]. Quantitive Finance, 2001, 1(4):382-387.

[3] Jarrow R A, Yu Fan. Counterparty risk and the pricing of default securities[J]. The Journal of Finance,2001, 56(5):1765-1799.

[4] Frey R, Backhaus J. Interacting defaults and counterparty risk:A markovian approach[R]. Working Paper:Department of Mathematics, University of Leipzig, 2003.

[5] 白云芬,胡新华,叶中行.关于双曲衰减的违约相关模型及CDS定价[J].应用数学和力学,2007, 28(12):1468-1474.

[6] Egloff D, Leippold M, Vanini P. A simple model of credit contagion[J]. Journal of Banking & Finance, 2007, 31(8):2475-2492.

[7] 王倩,Hartmannwendels T.信用违约风险传染建模[J].金融研究,2008,(10):162-173.

[8] 韩立岩,陈文丽.贷款组合中违约传染的机理研究[J].金融研究,2006,(7):143-150.

[9] 郑玉华,张涤新.贷款组合中违约传染的ACD模型[J].中国科学技术大学学报,2009, 39(12):1272-1276.

[10] Allen F, Carletti E. Credit risk transfer and contagion[J]. Journal of Monetary Economics, 2006, 53(1):89-111.

[11] Morrison A D. Credit derivatives, disintermediation and investment decisions[J]. Journal of Business, 2005, 78(2):621-647.

[12] Arping S. Playing hardball:Relationship banking in the age of credit derivatives[R].Working Paper,University of Amsterdam, 2002.

[13] Duffee G R, Zhou Chunsheng. Credit derivatives in banking:useful tools for managing risk[J]. Journal of Monetary Economics, 2001, 48(1):25-54.

[14] Chiesa G, Bhattacharya S. Optimal credit risk transfer, monitored finance, and real investment activity[R].Working Paper, University of Bologna,2007.

[15] Nijskens R, Wagner W. Credit risk transfer activities and systemic risk:How banks became less risky individually but posed greater risks to the financial system at the same time[J]. Journal of Banking & Finance, 2010, 34(10):1-8.

[16] 赵俊强,韩琳,李湛.信用风险转移与银行系统表现-基于美国信用衍生品交易市场面板数据的实证研究[J].金融研究,2007,(5):147-160.

[17] 陈庭强,何建敏,尹群耀,等.基于银行信贷行为的CRT市场信用风险传染评述[J].金融理论与实践,2011,(11):95-99.

[18] Clarke G, Langley R, Cardwell W. Empirical applications of dynamic spatial interaction models[J]. Computational Environmental and Urban Systems, 1998, 22(2):157-184.

[19] Celik H M, Guldmann J M. Spatial interaction modeling of interregional commodity flows[J]. Socio-Economic Planning Sciences, 2007, 41(2):147-162.

[20] O'Kelly M E. Entropy-Based Spatial Interaction Models for Trip Distribution[J]. Geographical Analysis, 2010, 42(4):472-487.

[21] Gordon I. Entropy, variety, economics, and spatial interaction[J]. Geographical Analysis,2010,42(4):446-471.

[22] Barro D, Basso A. Credit contagion in a network of firms with spatial interaction[J]. European Journal of Operational Research, 2010, 205(2):459-468.

[23] Carling K, Lundberg S. Asymmetric information and distance:An empirical assessment of geographical credit rationing[J]. Journal of Economics and Business, 2005, 57(1):39-59.

[24] Krawiecki A, Ho?yst J A. Stochastic resonance as a model for financial market crashes and bubbles[J]. Physica A:Statistical Mechanics and its Applications, 2003, 317(3):597-608.

[25] 李红权,马超群.金融市场的复杂性与风险管理[M].北京:经济科学出版社,2006.

[26] Gao Qin, Ma Junhai. Chaos and Hopf bifurcation of a finance system[J]. Nonlinear Dynamics, 2009,58(1-2):209-216.

[27] Schwarcz S L. Regulating complexity in financial markets[J]. Washington University Law Review, 2009, 87(2):211.

[28] Chen Tingqiang, He Jianmin, Wang Jining. Bifurcation and Chaotic Behavior of Credit Risk Contagion Based on FitzHugh-Nagumo System[J]. International Journal of Bifurcation and Chaos, 2013, 23(7):1350117.

[29] Corporation H P. Dynamics evolution of credit risk contagion in the CRT market[J]. Discrete Dynamics in Nature and Society, 2013, 32(1):412-423.

[30] Krugman P. Development, geography and economic theory[M]. Cambridge:MIT Press, 1997.

[31] Fujita M, Krugman P, Venables A J. The spatial economy:Cites, regions and international trade[M]. Cambridge:MIT Press, 1999.

[32] 尹虹潘.对城市吸引区范围界定的理论分析[J].财经研究,2005,31(11):108-114.

[33] Basso A, Barro D. Counterparty risk:A credit contagion model for a bank loan portfolio[J]. The ICFAI Journal of Financial Risk Management, 2005, 2(4):1-24.

基于熵空间交互理论的CRT网络信用风险传染模型

Spatial Interaction Theory-based Credit Risk Contagion Model for the CRT Network

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