融合CDS网络的银行间信用风险传染模型研究

doi:10.16381/j.cnki.issn1003-207x.2019.1033

摘要/Abstract

摘要： 信用违约互换（Credit Default Swap，CDS）作为银行信用风险缓释的重要工具，也是信用风险传染的重要渠道。因此，考虑到CDS抑制效应与传染效应的双重作用，本文构建了一个含有CDS网络交互作用的银行间信用风险传染模型，并运用计算实验与仿真模拟分析了在不同网络结构下银行网络信用风险传染与CDS网络抑制率，以及CDS网络信用风险抑制效应与银行网络传染率之间的关系，以及信用风险传染下银行网络和CDS网络的演化特征。研究结果表明：（1）银行网络的信用风险传染阈值与CDS网络抑制率呈单调递增的正相关系。而且，与同质网络结构相比，异质网络结构下的银行网络信用风险传染阈值相对较小。（2）CDS网络的信用风险抑制阈值与银行网络传染率呈单调递增正相关关系。而且，与同质网络结构相比，异质网络结构下CDS网络的信用风险抑制阈值明显较大。（3）银行网络最终破产规模与CDS抑制率呈单调递减的负相关关系。而且，异质网络结构下的银行破产规模要大于同质网络结构下的银行破产规模。（4）CDS网络破产规模与银行传染率呈单调递增的正相关关系。而且，同质网络结构下的CDS网络破产规模大于异质网络结构下的CDS网络破产规模。

关键词: 信用违约互换, 信用风险传染, 抑制效应, 传染效应, 网络结构

Abstract: Through payment, settlement, bill, loan and other businesses, banks have established a connected inter-bank market, which amplifies the contagion effect and damage degree of credit risk. However, Credit Default Swap (CDS) is an effective way to mitigate the spillover effect of inter-bank credit risk. In this article, with the help of complex network theory, the inter-bank credit risk contagion effect and the credit risk inhibition effect of CDS on Banks are considered, and a two-layer network model of inter-bank credit risk contagion that integrates CDS network is constructed. The results show that: (1) the credit risk contagion threshold of the bank networkλ_B^*presents monotonically increasing characteristics with the increase of CDS network inhibition rateλ_C. Moreover, compared with homogeneous network structure, the contagion threshold of bank networkλ_B^*credit risk under heterogeneous network structure is relatively small. (2) The credit risk inhibition threshold of the CDS networkλ_C^*presents a monotonically increasing feature with the increase of the contagion rate of the bank networkλ_B. Moreover, the credit risk suppression threshold of CDS networksλ_C^*with homogeneous network structure is higher than that of CDS networks with heterogeneous network structure. (3) With the increase of CDS inhibition rateλ_C, the final bankruptcy scale of the bank networkR_B(∞)presents a monotonic decreasing characteristic; withthe increase of bank infection rateλ_B, it presents a monotonic increasing characteristic. Moreover, the scale of bank bankruptcyR_B(∞)under heterogeneous network structure is larger than that under homogeneous network structure. (4) With the increase of bank contagion rateλ_B, the bankruptcy scale of CDS network presents a monotonically increasing feature. Moreover, the bankruptcy scale of CDS networkR_C(∞)under homogeneous network structure is larger than that under heterogeneous network structure.The model and research results designed in this paper will provide new ideas for the application of two-layer network in the study of Banks and CDS counterparty credit risk contagion.

Key words: credit default swap, contagion of credit risk, restraining effect, contagion effect, the network structure

中图分类号:

F830
F276

陈庭强, 周文静, 童毛弟, 刘海飞. 融合CDS网络的银行间信用风险传染模型研究[J]. 中国管理科学, 2020, 28(6): 24-37.

CHEN Ting-qiang, ZHOU Wen-jing, TONG Mao-di, LIU Hai-fei. Research on the Model of Inter-bank Credit Risk Contagion by Fusing CDS Networks[J]. Chinese Journal of Management Science, 2020, 28(6): 24-37.

参考文献

[1] 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.
[2] 方意, 郑子文. 系统性风险在银行间的传染路径研究——基于持有共同资产网络模型[J]. 国际金融研究, 2016, (6):61-72.
[3] Affinito M. The interbank network across the Global Financial Crisis:Evidence from Italy[J]. Journal of Banking & Finance, 2017, 80:90-107.
[4] Brownlees C, Engle R F. SRISK:A conditional capital shortfall measure of systemic risk[J]. ESRB Working Paper Series, 2017, 30(1):48-79.
[5] 陈庭强,李心丹,王冀宁.多因素耦合下CRT市场信用风险传染的熵空间模型[J].系统工程理论与实践,2016,36(1):1-14.
[6] 杨海军, 胡敏文. 基于核心-边缘网络的中国银行风险传染[J]. 管理科学学报, 2017, 20(10):49-61.
[7] Leventides J,Loukaki K. Simulating financial contagion dynamics in random interbank networks[J]. Journal of Economic Behavior & Organization, 2019, 158:500-525.
[8] Ladley D. Contagion and risk-sharing on the inter-bank market[J]. Journal of Economic Dynamics & Control, 2013, 37(7):1384-1400.
[9] González-Avella J C, Quadros V H, Iglesias J R. Network topology and interbank credit risk[J]. Chaos, Solitons& Fractals, 2016, 88:235-243.
[10] Keith H, Erin S. Credit risk transfer markets:an australian perspective[R]. Working Paper, Reserve Bank of Australian, 2003.
[11] Cebenoyan A S,Strahan P E. Risk management, capital structure and lending at banks[J]. Journal of Banking & Finance, 2004, 28(1):19-43.
[12] Elenora W, Vanessa R. Innovative credit risk transfer instruments and financial stability in austria[R]. Working Paper, Financial Stability Report, 2004.
[13] Chen Tingqiang, Li Xindan, Wang Jining. Spatial interaction model of credit risk contagion in the CRT market[J].Computational Economics, 2015, 46(4):519-537.
[14] Chen Tingqiang, He Jianmin,Li Xindan. An evolving network model of credit risk contagion in the financial market[J]. Technological & Economic Development of Economy, 2017, 23(1):1-16.
[15] Al-Own B, Minhat M, Gao S. Stock options and credit default swaps in risk management[J]. Jouranl of International Fianancial Markets, Institutions & Money, 2018, 53:200-214.
[16] Allen F,Carletti E.Credit risk transfer and contagion[J]. Journal of Monetary Economics, 2005, 53(1):89-111.
[17] Parlour C A, Winton A. Laying off credit risk:Loan sales versus credit default swaps[J]. Journal of Financial Economics, 2013, 107(1):25-45.
[18] Wagner W, Marsh I. Credit risk transfer and financial sector performance[J]. Journal of Financial Stability, 2006, 2(2):173-193.
[19] Lee S H. Systemic liquidity shortages and interbank network structures[J]. Journal of Financial Stability, 2013, 9(1):1-12.
[20] Langfield S, Liu Z, Ota T. Mapping the UK interbank system[J]. Journal of Banking & Finance, 2014, 45:288-303.
[21] Xu Tao, He Jianmin, Li Shouwei. A dynamic network model for interbank market[J]. Physica A:Statistical Mechanics and its Applications, 2016, 463:131-138.
[22] 唐振鹏, 谢智超, 冉梦,等. 网络视角下我国上市银行间市场系统性风险实证研究[J]. 中国管理科学, 2016,24(s1):500-505.
[23] 隋聪, 王宪峰, 王宗尧. 银行间网络连接倾向异质性与风险传染[J]. 国际金融研究, 2017, 363(7):46-55.
[24] Chen Tingqiang, Wang Jiepeng, Liu Haifei, et al. Contagion model on counterparty credit risk in the CRT market by considering the heterogeneity of counterparties and preferential-random mixing attachment[J].Physica A:Statistical Mechanics and its Applications, 2019, 520:458-480.
[25] Wang Lei, Li Shouwei,Chen Tingqiang. Investor behavior, information disclosure strategy and counterparty credit risk contagion[J]. Chaos, Solitons& Fractals, 2019, 119:37-49.
[26] Krause A, Giansante S. Interbank lending and the spread of bank failures:A network model of systemic risk[J]. Journal of Economic Behavior & Organization, 2012, 83(3):583-608.
[27] Battiston S, Gatti D, Gallegati M, et al. Liaisons dangereuses:Increasing connectivity, risk sharing, and systemic risk[J].Journal of Economic Dynamics and Control, 2012, 36(8):1121-1141.
[28] Bargigli L, Gallegati M. Finding communities in credit networks[J].Economics:The Open-Access, Open-Assessment E-Journal, 2013, 17(7):1-39.
[29] Mariya T. Systemic risk in banking networks:Advantages of "tiered" banking systems[J].Journal of Economic Dynamics and Control,2014,47:186-210.
[30] Acharya V, Bisin A. Counterparty risk externality:Centralized versus over-the-counter markets[J]. Journal of Economic Theory, 2014, 149(1):153-182.
[31] Bo L, Capponi A. Counterparty risk for CDS:Default clustering effects[J]. Journal of Banking & Finance, 2015, 52:29-42.
[32] Allen F, Gale D. Financial contagion[J]. Journal of Political Economy, 2000, 108(1):1-33.
[33] D'ErricoM,Battiston S, PeltonenT,et al. How does risk flow in the credit default swap market?[J]. Journal of Financial Stability, 2018, 35:53-74.
[34] Braverman A, Minca A. Networks of common asset holdings:Aggregation and measures of vulnerability[J]. Social Science Electronic Publishing, 2018,4(3):53-78.
[35] Cocco J F, Gomes F J, Martins N C. Lending relationships in the interbank market[J]. Journal of Financial Intermediation, 2009, 18(1):24-48.
[36] TemizsoyA,Iori G, Montes-Rojas G. The role of bank relationships in the interbank market[J]. Journal of Economic Dynamics & Control, 2015, 59:118-141.
[37] Gai P, Kapadia S. Contagion in financial networks[J]. Proceedings Mathematical Physical & Engineering Sciences, 2010, 466(2120):2401-2423.
[38] 王晓枫, 廖凯亮, 徐金池. 复杂网络视角下银行同业间市场风险传染效应研究[J]. 经济学动态, 2015, (3):71-81.
[39] Cifuentes R,FerrucciG, Shin H S. Liquidity risk and contagion[J]. Journal of the European Economic Association, 2005, 3(2/3):556-566.
[40] Brunetti C, Harris J H,MankadS, et al. Interconnectedness in the interbank martket[J]. Journal of Financial Economics, 2019,133(2):520-538.
[41] 马君潞,范小云,曹元涛. 中国银行间市场双边传染的风险估测及其系统性特征分析[J]. 经济研究, 2007,(1):69-79+143.
[42] 鲍勤, 孙艳霞. 网络视角下的金融结构与金融风险传染[J]. 系统工程理论与实践, 2014, 34(9):2202-2211.
[43] 秦学志,孙晓琳,张康. 责任分层下考虑银行违约关联的存款保险定价[J]. 系统管理学报, 2012,(1):9-15.
[44] 吴承根. 中国版CDS的发展问题与对策[D]. 上海:上海交通大学, 2014.
[45] 金漫漫. 信用违约互换在我国商业银行信用风险管理的应用研究[D]. 天津:天津大学, 2014
[46] 杨星,胡国强. 交易对手信用违约事件与信用违约互换公允价值[J]. 系统工程理论与实践, 2013, 33(6):1389-1394.
[47] 邓超,陈学军.基于复杂网络的金融传染风险模型研究[J].中国管理科学,2014,22(11):11-18.
[48] 胡志浩,李晓花.复杂金融网络中的风险传染与救助策略——基于中国金融无标度网络上的SIRS模型[J].财贸经济,2017,38(4):101-114.
[49] Philip D, O'Neill. A tutorial introduction to Bayesian inference for stochastic epidemic models using Markov chain Monte Carlo Methods[J]. Mathematical Biosciences, 2002, 180(1-2):103-114.
[50] Gleeson J P. High-accuracy approximation of binary-state dynamics on networks[J]. Physical Review Letters, 2011, 107(6):068701.
[51] Volz E M. SIR dynamics in random networks with heterogeneous connectivity[J]. Journal of Mathematical Biology, 2008, 56(3):293-310.
[52] Miller J C, Slim A C,Volz E M. Edge-based compartmental modelling for infectious disease spread[J]. Journal of The Royal Society Interface, 2012, 9(70):890-906.
[53] Mézard M, Parisi G. The Bethe lattice spin glass revisited[J]. The European Physical Journal B-Condensed Matter and Complex Systems, 2001, 20(2):217-233.
[54] Newman M E J, Strogatz S H, Watts D J. Random graphs with arbitrary degree distributions and their applications[J]. Physical Review E, 2001, 64(2):026118.
[55] Karrer B, Newman M E J. Message passing approach for general epidemic models[J]. Physical Review E, 2010, 82(1):016101.
[56] 李睿琪, 王伟, 舒盼盼,等. 复杂网络上流行病传播动力学的爆发阈值解析综述[J]. 复杂系统与复杂性科学, 2016,13(1):1-39.
[57] 陈庭强. 金融市场信用风险传染的复杂性建模与分析[M].科学出版社,2017.
[58] Chen Tingqiang, Xiao Bingqing, Liu Haifei. Credit risk contagion in an evolving network model integrating spillover effects and behavioral interventions[J]. Complexity, 2018,(4):1-16.
[59] Arora N, Gandhi P, Longstaff F A. Counterparty credit risk and the credit default swap market[J]. Journal of Financial Economics, 2012, 103(2):280-293.
[60] Wang Wei, Tang Ming, Zhang Haifeng, et al. Epidemic spreading on complex networks with general degree and weight distributions[J]. Physical Review E Statistical Nonlinear & Soft Matter Physics, 2014,90(4):1-9.