基于动态Copula的企业集团信用风险传染效应研究
|
摘要
企业集团成员企业间基于业务关联、技术关联和财务关联的相互联系,构成了企业信用风险传染的重要渠道。金融机构如果缺乏对企业集团成员企业间信用风险传染的管理和防控意识,其后果可能是金融机构信贷资产遭受巨大经济损失。本文基于尾部相依性的视角构建动态协变量Joe-Clayton copula模型,实证分析企业集团成员企业信用风险传染问题。研究发现,信用风险在企业集团成员企业间存在传染效应,且这种信用风险传染效应呈现一定的动态特征;宏观经济因素和微观公司经营能力、财务状况对企业集团内部不同组别子企业间信用风险传染效应的影响从重要性程度和作用方向两个维度上存在差异。
Credit risk contagion is more likely to happen across the subsidiary firms in an enterprise group because of the various types of connections.The lack of sense in detecting and controlling this credit risk contagion may trigger the dramatical loss to the financial institutions.In this paper,we focus on the extreme co-movements of default in an enterprise group in China.First,the credit risk is measured by calculating Distance-to-Default based on KMV model.Then,the dynamic Joe-Clayton copula model,which provides the flexible tail-dependence parameters,is applied to analyze the credit risk contagion in enterprise group.Finally,both macro-economic and micro-economic factors are used to identify the most important drivers in credit risk contagion across pairwise firms in enterprise group.Our findings suggest that:(1)Credit risk contagion may occur via the tail-dependence of default across subsidiary firms in an enterprise group.(2)For some periods,the significant increase in credit risk contagions across pairwise firms is observed.Moreover,these credit risk contagions present the dynamic trends over time.(3)For different pairwise firms,macro-economic and micro-economic factors play different roles on credit risk contagions in enterprise group.The most important drivers of different pairs of subsidiary firms are not the same.Our paper contributes to the recent risk literature in two respects.First,we expand on a small number of related studies by providing evidence of credit risk contagion among the subsidiary firms at an enterprise group.Second,macro-economic and micro-economic factors are incorporated into a dynamic copula model to investigate the most important drivers in credit risk contagion.In such way,the needs of regulators for risk detection tools and eventual early risk warnings are met.
Credit risk contagion is more likely to happen across the subsidiary firms in an enterprise group because of the various types of connections.The lack of sense in detecting and controlling this credit risk contagion may trigger the dramatical loss to the financial institutions.In this paper,we focus on the extreme co-movements of default in an enterprise group in China.First,the credit risk is measured by calculating Distance-to-Default based on KMV model.Then,the dynamic Joe-Clayton copula model,which provides the flexible tail-dependence parameters,is applied to analyze the credit risk contagion in enterprise group.Finally,both macro-economic and micro-economic factors are used to identify the most important drivers in credit risk contagion across pairwise firms in enterprise group.Our findings suggest that:(1)Credit risk contagion may occur via the tail-dependence of default across subsidiary firms in an enterprise group.(2)For some periods,the significant increase in credit risk contagions across pairwise firms is observed.Moreover,these credit risk contagions present the dynamic trends over time.(3)For different pairwise firms,macro-economic and micro-economic factors play different roles on credit risk contagions in enterprise group.The most important drivers of different pairs of subsidiary firms are not the same.Our paper contributes to the recent risk literature in two respects.First,we expand on a small number of related studies by providing evidence of credit risk contagion among the subsidiary firms at an enterprise group.Second,macro-economic and micro-economic factors are incorporated into a dynamic copula model to investigate the most important drivers in credit risk contagion.In such way,the needs of regulators for risk detection tools and eventual early risk warnings are met.
引文
[1]Huang R D,Li Hang.Does the market dole out collective punishment?An empirical analysis of industry,geography,and Arthur Andersen’s reputation[J].Journal of Banking and Finance,2009,33:1255-1265.
[2]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.
[3]He Jia,Mao Xinyang,Rui O M,et al.Business groups in china[J].Journal of Corporate Finance,2013,22:166-192.
[4]Khanna T,Yafeh T.Business groups and Risk Sharing around the world[J].Journal of Business,2005,78(1):301-340.
[5]Cheunga Y,Rau R,Stouraitis A.Tunneling,propping and expropriation:Evidence from connected party transaction in Hong Kong[J].Journal of Financial Economics,2006,82:343-386.
[6]黄俊,陈信元,张天舒.公司经营绩效传染效应的研究[J].管理世界,2013,3:111-118.
[7]Das S R,Duffie D,Kapadia N,et al.Common failings:How corporate defaults are correlated[J].Journal of Finance,2007,62(1):93-117.
[8]Giesecke K,Weber S.Credit contagion and aggregate loss[J].Journal of Economic Dynamics and Control,2006,30:741-767.
[9]Dong Yinghui,Wang Guojing.Bilateral counterparty risk valuation for credit default swap in a contagion model using Markov chain[J].Economic Modelling,2014,40:91-100.
[10]谢尚宇,汪寿阳,周勇.金融危机下带传染效应的预报[J].管理科学学报,2011,2:141-151.
[11]陈林,周宗放.企业集团控制下的关联企业违约相关性度量研究[J].中国管理科学,2010,18(5):159-164.
[12]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.
[13]李丽,周宗放.企业集团信用风险动态传染机理研究[J].管理评论,2015,1:48-56.
[14]Focardi S M,Fabozzi F J.A percolation approach to modeling credit lossdistribution under contagion[J].The Journal of Risk,2004,7:75-93.
[15]Giesecke K,Weber S.Cyclical correlations,credit contagion,and portfolio losses[J].Journal of Banking and Finance,2004,28(12):3009-3036.
[16]Fenech J,Vosgha H,Shafik S.Loan default correlation using an Archimedean copula approach:A case for recalibration[J].Economic Modelling,2015,47:340-354.
[17]罗长青,朱慧明,欧阳资生.跳跃—扩散条件下信用风险相关性度量的变结构copula模型[J].中国管理科学,2014,(3):1-12.
[18]Joe H.Multivariate models and dependence concepts[M].London:Chapman and Hall,1997.
[19]Li Feng,Kang Yanfei.Improving forecasting performance using covariate-dependent copula models[J].International Journal of Forecasting,2018,34(3):456-476.
[20]Geweke J,Amisano G. Comparing and evaluating Bayesian predictive distributions of asset returns[J].International Journal of Forecasting,2010,26(2):216-230.
[21]王贞洁,王竹泉.经济危机、信用风险传染与营运资金融资结构[J].中国工业经济,2013,11:122-134.
[22]Merton R.On the pricing of corporate debt:the risk structure of interest rates[J].Journal of Finance,1974,29(2):449-470.
[23]Hertzel M G,Officer M S.Industry contagion in loan spreads[J].Journal of Financial Economics,2012,103:493-506.
[2]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.
[3]He Jia,Mao Xinyang,Rui O M,et al.Business groups in china[J].Journal of Corporate Finance,2013,22:166-192.
[4]Khanna T,Yafeh T.Business groups and Risk Sharing around the world[J].Journal of Business,2005,78(1):301-340.
[5]Cheunga Y,Rau R,Stouraitis A.Tunneling,propping and expropriation:Evidence from connected party transaction in Hong Kong[J].Journal of Financial Economics,2006,82:343-386.
[6]黄俊,陈信元,张天舒.公司经营绩效传染效应的研究[J].管理世界,2013,3:111-118.
[7]Das S R,Duffie D,Kapadia N,et al.Common failings:How corporate defaults are correlated[J].Journal of Finance,2007,62(1):93-117.
[8]Giesecke K,Weber S.Credit contagion and aggregate loss[J].Journal of Economic Dynamics and Control,2006,30:741-767.
[9]Dong Yinghui,Wang Guojing.Bilateral counterparty risk valuation for credit default swap in a contagion model using Markov chain[J].Economic Modelling,2014,40:91-100.
[10]谢尚宇,汪寿阳,周勇.金融危机下带传染效应的预报[J].管理科学学报,2011,2:141-151.
[11]陈林,周宗放.企业集团控制下的关联企业违约相关性度量研究[J].中国管理科学,2010,18(5):159-164.
[12]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.
[13]李丽,周宗放.企业集团信用风险动态传染机理研究[J].管理评论,2015,1:48-56.
[14]Focardi S M,Fabozzi F J.A percolation approach to modeling credit lossdistribution under contagion[J].The Journal of Risk,2004,7:75-93.
[15]Giesecke K,Weber S.Cyclical correlations,credit contagion,and portfolio losses[J].Journal of Banking and Finance,2004,28(12):3009-3036.
[16]Fenech J,Vosgha H,Shafik S.Loan default correlation using an Archimedean copula approach:A case for recalibration[J].Economic Modelling,2015,47:340-354.
[17]罗长青,朱慧明,欧阳资生.跳跃—扩散条件下信用风险相关性度量的变结构copula模型[J].中国管理科学,2014,(3):1-12.
[18]Joe H.Multivariate models and dependence concepts[M].London:Chapman and Hall,1997.
[19]Li Feng,Kang Yanfei.Improving forecasting performance using covariate-dependent copula models[J].International Journal of Forecasting,2018,34(3):456-476.
[20]Geweke J,Amisano G. Comparing and evaluating Bayesian predictive distributions of asset returns[J].International Journal of Forecasting,2010,26(2):216-230.
[21]王贞洁,王竹泉.经济危机、信用风险传染与营运资金融资结构[J].中国工业经济,2013,11:122-134.
[22]Merton R.On the pricing of corporate debt:the risk structure of interest rates[J].Journal of Finance,1974,29(2):449-470.
[23]Hertzel M G,Officer M S.Industry contagion in loan spreads[J].Journal of Financial Economics,2012,103:493-506.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |