交易对手信用风险研究
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摘要
近三十年来，大型商业银行一直处于传统存贷款商业银行向综合性全能商业银行的业务转型过程中。各大商业银行为增强自身的市场竞争力，设计出大量金融衍生品以满足不同客户的投融资需求。金融创新的不断深化使得整个社会资源配置效率逐步提高的同时，也提高了整个金融系统风险的传染性，金融机构面临的交易对手信用风险更加错综复杂。这是导致历次金融危机都表现出很强信用风险事件传染性的重要原因。2008年金融危机中，巴塞尔委员会因在交易对手信用风险监管上的漏洞而屡遭批评，为此巴塞尔委员会在其新发布的第三版巴塞尔资本协议中重点改进了对交易对手信用风险的监管,明确要求商业银行需要计算信用估值调整风险加权资产和交易对手信用风险违约风险加权资产，并分别纳入一般的市场风险加权资产和信用风险加权资产中。
本文基于境内金融市场的实践，对交易对手信用风险进行了全面的实证分析，并采用Copula函数，分析了不同市场风险因素之间的相关性对交易对手信用风险暴露的影响。本文首先梳理了金融衍生品的资产定价理论及资产价格与利率的随机模型，提出使用几何布朗运动和主成分分析模型分别对美元对人民币汇率以及美元利率与人民币利率进行模拟，然后对交易对手信用风险的理论和实践进行了全面的总结，包括交易对手信用风险的特性、风险缓释措施、错向风险、交易对手信用风险暴露的计量、信用估值调整与交易对手信用风险加权资产的计量以及交易对手信用风险限额等内容，并重点研究了Copula函数的性质、线性相关性与Copula函数相关性的度量、Copula函数的尾部相关性度量、Copula函数的构建与估计、Copula函数的选择与评价以及不同Copula函数的比较等内容，最后从境内金融市场的实践出发，选择境内金融市场中最为常见的外汇远期与利率掉期两类产品，采用现期风险暴露法和内部模型法分别计量交易对手信用风险暴露，采用标准法和等价债券法分别计量信用估值调整风险加权资产，采用权重法和内部评级法分别计量交易对手信用违约风险加权资产，并就净额结算协议和抵押品协议两类风险缓释措施对交易对手信用风险的缓释作用进行了实证分析，还基于正态Copula、 t Copula、 Gumbel Copula、Clayton Copula以及FrankCopula等五种Copula函数分析了美元对人民币汇率与美元利率以及人民币利率之间的相关性，并分别计量了存在相关性和不存在相关性时整个资产组合的预期风险暴露、信用估值调整和交易对手信用违约风险加权资产。
In recent30years, large commercial banks have been in the process oftransforming from traditional deposit and lending commercial banks to universalcommercial banks. To enhance their market competitiveness, large commercial bankshave being devoted to design a large number of financial derivatives in order to meetdifferent customers’ investing or financing needs. With the deepening of financialinnovation, on one hand, the society as a whole benefits from the efficiency ofresource allocation; on the other hand, the infectivity of the entire financial systemrisk also increases, and counterparty credit risk faced by financial institutions is moreand more complicated. This is an important cause of why recent financial crises haveshown strong infectiousness of credit risk event. During2008financial crisis, theBasel Committee had been repeatedly criticized for its loopholes on the supervision ofcounterparty credit risk. So Basel Committee focused on improving counterpartycredit risk regulation in its newly released third edition of the Basel Accord. TheAccord explicitly requires commercial banks to calculate risk-weighted assets forcredit valuation adjustment and default risk-weighted assets for counterparty credit,and include them into market risk-weighted asset and credit risk-weighted assetsrespectively.
This thesis based on the practice of the domestic financial market, conducts acomprehensive empirical study of counterparty credit risk, and using copula functionanalyzes the influences of the correlation between different market risk factors to theexposure to counterparty credit risk. This thesis firstly summarizes the asset pricingtheory on financial derivatives and stochastic models of asset prices and interest rates,and suggests using geometric Brownian motion model and principal componentsanalysis model in the simulation of the exchange rate of U.S. dollar against RMB andinterest rate of U.S. dollar and RMB. Then this thesis makes a comprehensivesummary of the theory and practice of counterparty credit risk, including itscharacteristics, risk mitigation measures, wrong-way risk, the measurement ofcounterparty credit risk exposure, the measurement of credit valuation adjustment anddefault risk-weighted assets for counterparty credit risk and counterparty credit risklimits, With an emphasis on the study of the nature of the copula function, the measurement of linear correlation and copula function correlation, the measurementof copula function tail correlation, construction and estimates of the copula function,the selection and evaluation of the copula function as well as the comparison of thedifferent copula functions. Finally, the thesis conducts three empirical studies. Thefirst study, with demonstration of the most common financial products of foreignexchange forwards and interest rate swaps in the domestic financial market, uses thecurrent exposure method and internal models method separately in measuring thecounterparty credit risk exposure, uses the standardised method and bond equivalentmethod separately in measuring risk-weighted assets for credit valuation adjustment,and uses the standardised method and the IRB measurement separately in measuringdefault risk-weighted assets for counterparty credit risk. The second study poses anempirical analysis on the effects of two types of risk mitigation measures--nettingagreements and collateral agreements--on the counterparty credit risk. The third studyanalyzes the correlation of the exchange rate of U.S. dollar against RMB and the U.S.dollar interest rate, and of the exchange rate of U.S. dollar against RMB and RMBinterest rate, based on Gaussian Copula, t Copula, Gumbel Copula, Clayton Copulaand Frank Copula. The study also measures the expected exposure of the entireportfolio, the credit valuation adjustment and default risk-weighted assets forcounterparty credit risk when correlation exists and not respectively.
本文基于境内金融市场的实践，对交易对手信用风险进行了全面的实证分析，并采用Copula函数，分析了不同市场风险因素之间的相关性对交易对手信用风险暴露的影响。本文首先梳理了金融衍生品的资产定价理论及资产价格与利率的随机模型，提出使用几何布朗运动和主成分分析模型分别对美元对人民币汇率以及美元利率与人民币利率进行模拟，然后对交易对手信用风险的理论和实践进行了全面的总结，包括交易对手信用风险的特性、风险缓释措施、错向风险、交易对手信用风险暴露的计量、信用估值调整与交易对手信用风险加权资产的计量以及交易对手信用风险限额等内容，并重点研究了Copula函数的性质、线性相关性与Copula函数相关性的度量、Copula函数的尾部相关性度量、Copula函数的构建与估计、Copula函数的选择与评价以及不同Copula函数的比较等内容，最后从境内金融市场的实践出发，选择境内金融市场中最为常见的外汇远期与利率掉期两类产品，采用现期风险暴露法和内部模型法分别计量交易对手信用风险暴露，采用标准法和等价债券法分别计量信用估值调整风险加权资产，采用权重法和内部评级法分别计量交易对手信用违约风险加权资产，并就净额结算协议和抵押品协议两类风险缓释措施对交易对手信用风险的缓释作用进行了实证分析，还基于正态Copula、 t Copula、 Gumbel Copula、Clayton Copula以及FrankCopula等五种Copula函数分析了美元对人民币汇率与美元利率以及人民币利率之间的相关性，并分别计量了存在相关性和不存在相关性时整个资产组合的预期风险暴露、信用估值调整和交易对手信用违约风险加权资产。
In recent30years, large commercial banks have been in the process oftransforming from traditional deposit and lending commercial banks to universalcommercial banks. To enhance their market competitiveness, large commercial bankshave being devoted to design a large number of financial derivatives in order to meetdifferent customers’ investing or financing needs. With the deepening of financialinnovation, on one hand, the society as a whole benefits from the efficiency ofresource allocation; on the other hand, the infectivity of the entire financial systemrisk also increases, and counterparty credit risk faced by financial institutions is moreand more complicated. This is an important cause of why recent financial crises haveshown strong infectiousness of credit risk event. During2008financial crisis, theBasel Committee had been repeatedly criticized for its loopholes on the supervision ofcounterparty credit risk. So Basel Committee focused on improving counterpartycredit risk regulation in its newly released third edition of the Basel Accord. TheAccord explicitly requires commercial banks to calculate risk-weighted assets forcredit valuation adjustment and default risk-weighted assets for counterparty credit,and include them into market risk-weighted asset and credit risk-weighted assetsrespectively.
This thesis based on the practice of the domestic financial market, conducts acomprehensive empirical study of counterparty credit risk, and using copula functionanalyzes the influences of the correlation between different market risk factors to theexposure to counterparty credit risk. This thesis firstly summarizes the asset pricingtheory on financial derivatives and stochastic models of asset prices and interest rates,and suggests using geometric Brownian motion model and principal componentsanalysis model in the simulation of the exchange rate of U.S. dollar against RMB andinterest rate of U.S. dollar and RMB. Then this thesis makes a comprehensivesummary of the theory and practice of counterparty credit risk, including itscharacteristics, risk mitigation measures, wrong-way risk, the measurement ofcounterparty credit risk exposure, the measurement of credit valuation adjustment anddefault risk-weighted assets for counterparty credit risk and counterparty credit risklimits, With an emphasis on the study of the nature of the copula function, the measurement of linear correlation and copula function correlation, the measurementof copula function tail correlation, construction and estimates of the copula function,the selection and evaluation of the copula function as well as the comparison of thedifferent copula functions. Finally, the thesis conducts three empirical studies. Thefirst study, with demonstration of the most common financial products of foreignexchange forwards and interest rate swaps in the domestic financial market, uses thecurrent exposure method and internal models method separately in measuring thecounterparty credit risk exposure, uses the standardised method and bond equivalentmethod separately in measuring risk-weighted assets for credit valuation adjustment,and uses the standardised method and the IRB measurement separately in measuringdefault risk-weighted assets for counterparty credit risk. The second study poses anempirical analysis on the effects of two types of risk mitigation measures--nettingagreements and collateral agreements--on the counterparty credit risk. The third studyanalyzes the correlation of the exchange rate of U.S. dollar against RMB and the U.S.dollar interest rate, and of the exchange rate of U.S. dollar against RMB and RMBinterest rate, based on Gaussian Copula, t Copula, Gumbel Copula, Clayton Copulaand Frank Copula. The study also measures the expected exposure of the entireportfolio, the credit valuation adjustment and default risk-weighted assets forcounterparty credit risk when correlation exists and not respectively.
引文
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①来源：2011年巴塞尔委员会《巴塞尔委员会确定双边交易对手信用风险资本要求》。
①来自于巴塞尔协议III：AGlobal Regulatory Framework for More Resilient Banks and Banking Systems。
①来自于巴塞尔协议III：AGlobal Regulatory Framework for More Resilient Banks and Banking Systems。
①来自于巴塞尔协议III：AGlobal Regulatory Framework for More Resilient Banks and Banking Systems。
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[51] Kamtchueng, C.“CVA, Premium or Charge? Call Hedging”,2011, Working Paper,http://ssrn.com/abstract=2005310.
[52] Kendall, M. G.“A New Measure of Rank Correlation”, Biometrika,30,1938.
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[58] Lu, D. and F. Juan，“An Efficient, Distributable, Risk Neutral Framework for CVACalculation”, The IUP Journal of Financial Risk Management,2011.
[59] Matteis, R. D.“Fitting Copulas to Data”, Institute of Mathematics of the University of Zurich,2001.
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②Basel Committee on Banking Supervision, International Convergence of Capital Measurement and CapitalStandards,2006.
①Basel Committee on Banking Supervision, International Convergence of Capital Measurement and CapitalStandards,2006.
①Basel Committee on Banking Supervison, International Convergence of Capital Measurement and CapitalStandards,2006.
①Nelson, R. B. An Introduction to Copulas, New York:Springer,1999.
②Sklar, A.“Fonctions de Repartition a n Dimensions et Leurs Marges”, Publications de1’ Institut de Statistiquede1’ Universite de Pairs,1959,8. pp.229-231.
①Nelsen, R. B. An Introduction to Copulas, New York: Springer,2006.
②Kendall, M. G.“A New Measure of Rank Correlation”, Biometrika,30,1938.
③Kruskal, W. H.“Ordinal Measure of Association”, Journal of the American Statistical Association,53,1958.
④Hollander, M. and D. A. Wolfe, Nonparametric Statistical Methods, New York: John Wiley&Sons Ltd.1973.
⑤Lehmann, E. L. Nonparametrics: Statistical Methods Based on Ranks, Holden-Day, San Francisco,1975.
①Lehmann, E. L.“Some Concepts of Dependence”, The Annals of Mathematical Statistics,37,1966, pp.1137-1153.
①Lehmann, E. L.“Some Concepts of Dependence”, The Annals of Mathematical Statistics,37,1966, pp.1137-1153.
①Genest, C. and J. Mackay,“The Joy of Copulas: Bivariate Distributions with Uniform Marginals”, The AmericanStatistician,40,1986, pp.280-283.
①Hu, L. Essays in Econometrics with Applications in Macroeconomic and Financial Modeling, New Haven: YaleUniversity,2002.
②Hu, L.“Dependence Patterns across Financial Markets: a Mixed Copula Approach”, Applied FinancialEconomics, vol.16, issue10,2006, pp.717-729.
①来源：2011年巴塞尔委员会《巴塞尔委员会确定双边交易对手信用风险资本要求》。
①来自于巴塞尔协议III：AGlobal Regulatory Framework for More Resilient Banks and Banking Systems。
①来自于巴塞尔协议III：AGlobal Regulatory Framework for More Resilient Banks and Banking Systems。
①来自于巴塞尔协议III：AGlobal Regulatory Framework for More Resilient Banks and Banking Systems。
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[2]巴塞尔银行监管委员会，中国银行业监督管理委员会译，《第三版巴塞尔协议》，中国金融出版社，2011年7月。
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[5]史道济、姚庆祝，《改进Copula对数据拟合的方法》，《系统工程理论与实践》，2004年第4期，第49-55页。
[6]叶五一、缪柏其，《基于Copula变点检测的美国次级债金融危机传染分析》，《中国管理科学》，2009年第3期，第1-7页。
[7]司继文、蒙坚玲、龚朴，《国内外期货市场相关性研究》，《华中科技大学学报》，2004年第4期，第16-19页。
[8]司继文、蒙坚玲、龚朴，《国内外股票市场相关性的Copula分析》，《华中科技大学学报》，2005年第1期，第114-116页。
[9]任仙玲、叶明确、张世英，《资产相关结构对投资组合风险测度的影响分析》，《统计与决策》，2008年第19期，第38-40页。
[10]许瑾，缪柏其，《利率期限结构的主成分分析》，《数理统计与管理》，2004年第2期，第19-21页。
[11]何旭彪，《金融风险综合评估方法最新研究进展》，《国际金融研究》，2008年第6期，第63-68页。
[12]李娟、戴洪德、刘全辉，《几种Copula函数在沪深股市相关性建模中的应用》，《数学的实践与认识》，2007年第24期，第16-20页。
[13]李平、马婷婷，《基于Copula的我国商业银行整体风险度量》，《硅谷》，2008年第12期，第148-149页。
[14]李仲飞、姚京，《中国沪深股市整合性的实证分析》，《管理评论》，2004年第1期，第28-30页。
[15]肖振宇、胡挺，《金融控股公司的风险度量研究》，《中山大学研究生学刊》，2006年第3期，第83-95页。
[16]张尧庭，《连接函数Copula技术与金融风险分析》，《统计研究》，2002年第4期，第48-51页。
[17]赵丽琴、籍艳丽，《Copula函数的非参数核密度估计》，《统计与决策》，2009年第9期，第29-32页。
[18]侯成琪、王频，《基于连接函数的整合风险度量研究》，《统计研究》，2008年第11期，第72-78页。
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