CDO产品风险评估研究
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摘要
债权担保证券(Collateralized Debt Obligation, CDO)作为结构性产品的典型代表,在21世纪初获得突飞猛进的发展,交易品种和交易量迅速增长。2007年美国次债危机和随后波及全球的金融危机,又把CDO等结构性产品市场打入谷底。金融危机的爆发反映出一个基本事实:研究机构和结构性产品市场的参与者,没有充分认识和准确度量结构性产品的风险。同时,我国作为金融发展相对滞后的新兴市场经济国家,从2005年开始积极探索和试点结构性产品的发展。迄今为止,主要商业银行都发行过CDO类产品。基于以上认识,CDO产品不会因为次贷危机退出历史舞台,而是在吸取次贷危机的教训、准确度量风险的基础上获得稳健发展。本文选择CDO产品风险评估作为研究主题,分析现有风险度量方法和信用评级存在的问题和缺陷,在中国金融发展背景下提出分析CDO风险的研究框架和风险度量模型。
本论文研究的核心部分是尝试解决CDO风险评估的三个关键问题。第一,在运用股市数据计算资产收益率相关系数时,为避免与相关结构无关的信息对相关系数估计的影响,本文提出了FGARCH-ST、FGARCH-SGED和FGARCH-NIG三个模型来刻画单资产股票价格对数收益率序列的典型特征,然后采用Copula技术估计残差序列的相关系数。实证分析得出,FGARCH-NIG模型的结果最好。第二,为充分刻画尖峰肥尾和偏度等信用资产组合损失分布的典型特征,本文基于分布混合假设(MDH)和Vasicek模型构建了NIG-Copula因子模型、双NIG-Copula因子模型和三NIG-Copula因子模型。实证分析表明,与信用资产组合风险评估的标准模型——CreditMetrics相比,NIG-Copula因子模型不仅具有半解析、简洁的分析框架,而且能较好地反映信用资产组合损失分布的尖峰肥尾、偏度和尾部相关等特征,提高了极值VaR估计的准确性。第三,提出基于完整现金流分布的分券风险度量模型,充分利用完整现金流的所有信息,评估CDO分券的尾部风险。
本文还考察了结构性产品的界定和国际主要信用评级机构对结构性产品的信用评级。结构性产品的迅猛发展导致产品种类繁多和命名的混乱,这对结构性产品的认识和阅读金融数据造成很大的困扰。通过分析产品结构和风险特性,本文给出结构性产品的统一定义和与主要发达国家结构性产品统计相一致的产品分类。作为对本研究的一个重要补充,本文分析了CDO产品信用评级存在的问题。根据CDO产品信用评级的特点,重点考察了信用评级的模型风险和评级套利,以及内在利益冲突、透明度和竞争状况等衍生问题,从另一个角度得出对CDO风险认识不足的原因。
本文是在金融危机余波未尽、全球CDO产品市场陷入低谷,同时我国快速发展结构性产品的背景下,对CDO产品风险的一次探索性研究。利用后发优势,吸取发达国家CDO产品市场的经验教训,在充分认识和准确度量CDO风险的前提下稳健发展CDO市场是发展中国家金融发展的必由之路。本文提出的模型可进一步拓展到CDO的定价。
As a stylized representative of structured products, Collateralized Debt Obligation (CDO) has developed rapidly since the beginning of 21 century, with a fast growth of trading scale and varieties. While after the outburst of the subprime crisis in 2007 and subsequent financial crisis, the development of CDO markets is brought to a standstill. The breakout of the crisis discloses a basic fact that research institutes and participants of structured products market, have not fully recognized and measured the risk of structured products. At the same time, China, as an emerging market country which is lagged in financial development, began to explore and launch a pilot project of structured products in 2005. Up to now, most commercial banks have issued some structured products. Basing on aforementioned view, we can conclude that CDO will not step down from the stage of history, but continue to develop stably basing on the lessons from the subprime crisis and measuring the risk correctly. This paper chooses to research on risk measurement of CDO and has constructed a framework of risk analysis and risk measuring models of CDO.
The core of this paper tries to solve the three key problems of risk measuring of CDO. Firstly, In order to prevent information irrelative with correlation from influencing on the estimation of correlation when calculating asset return correlation from stock prices data, this paper put up with FGARCH-ST, FGARCH-SGED and FGARCH-NIG to capture the stylized characteristics of time series of log return of stock prices. And then estimate the correlation of residuals using Copula functions. Empirical analyses show that the results of FGARCH-NIG are best. Secondly, For the sake of capturing the stylized features of loss distribution of credit portfolio, such as fat-tail and skewedness, this paper constructs NIG-Copula factor model, double-NIG-Copula factor model and triple-NIG-Copula factor model basing on mixture- distribution-hypothesis and Vasicek model. Empirical analyses show that NIG-Copula factor model is not only semi analytical and compact framework, but also better to describe the fat tail, skewedness and tail correlation of the loss distribution, comparing with CreditMetrics, which is standard model to measure credit portfolio risk. These models contribute to estimating extreme VaR accurately. Thirdly, this paper puts forward a risk measuring model of tranches basing on analyzing full cash flow and measures the tail risk using all information about cash flow.
This paper also explores the definition of structured products and credit ratings made by main international credit rating agencies. The fast development of structured products leads to a great variety of products and a confusing nomenclature. It is hard to understand these complex financial products and read relative statistical data. This paper gives a uniform definition by analyzing structure and risk characteristics of structured products. As an important complement of our research, this paper explores credit rating on CDO by main international credit rating agencies。Model risk, rating arbitrage, conflict of interest, and competition are analyzed mainly. We get the reason why the risk of CDO has not been understood fully from the rating angle.
This paper is an exploring research on the risk of CDO under the background of the aftermath of financial crisis not calming down, global CDO markets keeping a standstill, while structured products developing fast in China. It is the only way to develop CDO market in lagged and developing countries on the precondition of utilizing advantages of backwardness fully, learning a lesson of CDO market in developed countries and measuring the risk of CDO correctly. The models proposed in this paper can be extended to pricing of CDO.
本论文研究的核心部分是尝试解决CDO风险评估的三个关键问题。第一,在运用股市数据计算资产收益率相关系数时,为避免与相关结构无关的信息对相关系数估计的影响,本文提出了FGARCH-ST、FGARCH-SGED和FGARCH-NIG三个模型来刻画单资产股票价格对数收益率序列的典型特征,然后采用Copula技术估计残差序列的相关系数。实证分析得出,FGARCH-NIG模型的结果最好。第二,为充分刻画尖峰肥尾和偏度等信用资产组合损失分布的典型特征,本文基于分布混合假设(MDH)和Vasicek模型构建了NIG-Copula因子模型、双NIG-Copula因子模型和三NIG-Copula因子模型。实证分析表明,与信用资产组合风险评估的标准模型——CreditMetrics相比,NIG-Copula因子模型不仅具有半解析、简洁的分析框架,而且能较好地反映信用资产组合损失分布的尖峰肥尾、偏度和尾部相关等特征,提高了极值VaR估计的准确性。第三,提出基于完整现金流分布的分券风险度量模型,充分利用完整现金流的所有信息,评估CDO分券的尾部风险。
本文还考察了结构性产品的界定和国际主要信用评级机构对结构性产品的信用评级。结构性产品的迅猛发展导致产品种类繁多和命名的混乱,这对结构性产品的认识和阅读金融数据造成很大的困扰。通过分析产品结构和风险特性,本文给出结构性产品的统一定义和与主要发达国家结构性产品统计相一致的产品分类。作为对本研究的一个重要补充,本文分析了CDO产品信用评级存在的问题。根据CDO产品信用评级的特点,重点考察了信用评级的模型风险和评级套利,以及内在利益冲突、透明度和竞争状况等衍生问题,从另一个角度得出对CDO风险认识不足的原因。
本文是在金融危机余波未尽、全球CDO产品市场陷入低谷,同时我国快速发展结构性产品的背景下,对CDO产品风险的一次探索性研究。利用后发优势,吸取发达国家CDO产品市场的经验教训,在充分认识和准确度量CDO风险的前提下稳健发展CDO市场是发展中国家金融发展的必由之路。本文提出的模型可进一步拓展到CDO的定价。
As a stylized representative of structured products, Collateralized Debt Obligation (CDO) has developed rapidly since the beginning of 21 century, with a fast growth of trading scale and varieties. While after the outburst of the subprime crisis in 2007 and subsequent financial crisis, the development of CDO markets is brought to a standstill. The breakout of the crisis discloses a basic fact that research institutes and participants of structured products market, have not fully recognized and measured the risk of structured products. At the same time, China, as an emerging market country which is lagged in financial development, began to explore and launch a pilot project of structured products in 2005. Up to now, most commercial banks have issued some structured products. Basing on aforementioned view, we can conclude that CDO will not step down from the stage of history, but continue to develop stably basing on the lessons from the subprime crisis and measuring the risk correctly. This paper chooses to research on risk measurement of CDO and has constructed a framework of risk analysis and risk measuring models of CDO.
The core of this paper tries to solve the three key problems of risk measuring of CDO. Firstly, In order to prevent information irrelative with correlation from influencing on the estimation of correlation when calculating asset return correlation from stock prices data, this paper put up with FGARCH-ST, FGARCH-SGED and FGARCH-NIG to capture the stylized characteristics of time series of log return of stock prices. And then estimate the correlation of residuals using Copula functions. Empirical analyses show that the results of FGARCH-NIG are best. Secondly, For the sake of capturing the stylized features of loss distribution of credit portfolio, such as fat-tail and skewedness, this paper constructs NIG-Copula factor model, double-NIG-Copula factor model and triple-NIG-Copula factor model basing on mixture- distribution-hypothesis and Vasicek model. Empirical analyses show that NIG-Copula factor model is not only semi analytical and compact framework, but also better to describe the fat tail, skewedness and tail correlation of the loss distribution, comparing with CreditMetrics, which is standard model to measure credit portfolio risk. These models contribute to estimating extreme VaR accurately. Thirdly, this paper puts forward a risk measuring model of tranches basing on analyzing full cash flow and measures the tail risk using all information about cash flow.
This paper also explores the definition of structured products and credit ratings made by main international credit rating agencies. The fast development of structured products leads to a great variety of products and a confusing nomenclature. It is hard to understand these complex financial products and read relative statistical data. This paper gives a uniform definition by analyzing structure and risk characteristics of structured products. As an important complement of our research, this paper explores credit rating on CDO by main international credit rating agencies。Model risk, rating arbitrage, conflict of interest, and competition are analyzed mainly. We get the reason why the risk of CDO has not been understood fully from the rating angle.
This paper is an exploring research on the risk of CDO under the background of the aftermath of financial crisis not calming down, global CDO markets keeping a standstill, while structured products developing fast in China. It is the only way to develop CDO market in lagged and developing countries on the precondition of utilizing advantages of backwardness fully, learning a lesson of CDO market in developed countries and measuring the risk of CDO correctly. The models proposed in this paper can be extended to pricing of CDO.
引文
[1] Mandelbrot B B. The variation of certain specultive prices[J]. Journal of Business. 1963(36): 392-417.
[2] Fama F. The behavior of stock prices[J]. Journal of Business. 1965(38): 34-105.
[3] Georgieva A. The use of structured products: applications, benefits and limitations for the institutional investor[R]. Zurich: University of Zurich, 2005.
[4] Hens T, Rieger M O. The Dark Side of the Moon: Structured Products from the Customer’s Perspective[J]. working paper, Zurich: University Zurich. 2009.
[5] Rajan A, Mcdermott G, Roy R. The Structured Credit Handbook[M]. New York: John Wiley & Sons Inc, 2007: 1-11.
[6]法博兹弗兰克J.,休亨瑞莫德.欧洲结构金融产品手册[M].北京:中国金融出版社, 2006.
[7] Blundell-Wignall A. Structured Products: Implications for Financial Markets[J]. Financial Market Trends. 2007(2): 1-93.
[8] Whetten M, Adelson M. CDOs-squared Demystified[R]. Nomura Fixed Income Research, 2007.
[9] Lucas D J, Goodman L S, Fabozzi F J. Collateralized Debt Obligations and Credit Risk Transfer[R]. New Haven: Yale University, 2007.
[10] Wilde J P K A. Risk Transfer with CDOs[R]. Frankfurt: Frankfurt University's Center for Financial Studies, 2008.
[11] Deventer D R. CDOs and the Credit Crisis Complexity and model risk in the collateralized debt obligation market are severe[R]. Hawaii: Kamakura Corporation, 2008.
[12] Hull J C. The Credit Crunch of 2007: What Went Wrong? Why? What Lessons Can Be Learned[J]. Journal of Credit Risk. 2009, 5(2): 3-18.
[13] Crouhy M G, Jarrow R A, Turnbull S M. The Subprime Credit Crisis of 07[J]. Journal of Derivatives. 2008, 16(1): 81-111.
[14] Merton R C. On the pricing of corporate debt: the risk structure of interest rates[J]. Journal of Finance. 1974, 29: 449-470.
[15] Kim J, Ramaswamy K, Sundaresan S. Does Default Risk in Coupons Affect theValuation of Corporate Bonds? A Contingent Claims Model[J]. Financial Management. 1993, 22: 117-131.
[16] Longstaff F, Schwartz E. A Simple Approach to Value Risky Fixed and Floating Rate Debt[J]. Journal of Finance. 1995, 50: 789-819.
[17] Mason S, Bhattacharya S. Risk Debt, Jump Process, and Safety Covenants[J]. Journal of Financial Economics. 1981(9): 281-307.
[18] Sch?nbucher P. The Term Structure of Defaultable Bond Prices[J]. Review of Derivatives Research. 1998(6): 161-192.
[19] Leland H. Corporate Debt Value, Bond Covenants, and Optimal Structure[J]. Journal of Finance. 1994, 49: 1213-1252.
[20] Uhrig M, Walter U. A New Numerical Approach for Fitting the Initial Yield Curve[J]. Journal of Fixed Income. 1996(5): 82-90.
[21] Anderson R, Sundaresan S. Design and Valuation of Debt Contracts[J]. Review of Financial Studies. 1996(9): 37-68.
[22] Fan H, Sundaresan S. Debt Valuation, Strategic Debt Service and Optimal Dividend Policy [J]. Review of Financial Studies. 2000, 13(1057-1099).
[23] Jarrow R, Turnbull S. Pricing Derivatives on Financial Securities Subject to Credit Risk[J]. Journal of Finance. 1995, 50: 53-85.
[24] Jarrow R, Lando D, Turnbull S. A Markov Model for the Term Structure of Credit Risk Spreads[J]. Review of Financial Studies. 1997(10): 481-523.
[25] Madan D, Unal H. Pricing the Risk of Default[J]. Review of Derivatives Research. 1998(2): 121-160.
[26] Lando D. On Cox Processes and Credit Risky Securities[J]. Review of Derivatives Research. 1998(2): 99-120.
[27] Duffie D, Singleton K. Modeling Term Structure of Default Risk Bonds[J]. Review of Financial Studies. 1999(12): 687-720.
[28] Abid F, Naifar N. CDO Parameters Estimation Using Market Prices[J]. International Research Journal of Finance and Economics. 2008(18): 85-95.
[29] Hrvatin R, Neugebauer M. Default correlation and its effect on portfolio of credit risk[R]. New York: FitchRatings, 2004.
[30] Lee M, Beinstein E, Ahluwalia R, et al. Credit correlation: a guide[R]. London: JPMorgan, Credit Derivatives Strategy, 2004.
[31] Nelsen R B. An Introduction to Copulas[M]. Second Edition ed. New York: Springer, 2006.
[32] Embrechts P, Mcneil A, Straumann D. Correlation: Pitfalls and Alternatives[J]. Risk. 1999(12): 69-71.
[33] Embrechts P, Lindskog F, Mcneil A. Modeling Dependence with Copulas and Applications to Risk Management[M]. Handbook of Heavy Tailed Distributions in Finance, Rachev S, New Jersy: Elsevier, 2003, 329-384.
[34] Patton A. modeling Asymmetric Exchange Rate Dependence[J]. International Economic Review. 2006, 47(2): 527-556.
[35] Cherubini U, Luciano E, Vecchiato W. Copula Methods in Finance[M]. England: John Wiley & Sons Ltd, 2004.
[36] Genest C, Ghoudi K, Rives L P. A Semiparametric Estimation Procedure of Dependence Parameters in Multivariate Families of Distribution[J]. Biometrika. 1995, 82: 543-552.
[37] Embrechts P. Copulas: A personal view[J]. Journal of Risk and Insurance. 76, 639-650.
[38] Clemente D A, Romano C. Measuring Portfolio Value-at-risk by Copula-EVT based Approach. working paper. 2003.
[39]张尧庭.连接函数(Copola)技术与金融风险分析[J].统计研究. 2002(4): 48-51.
[40]张明恒.多金融资产风险价值的Copula计量方法研究[J].数量经济技术经济研究. 2004(4): 67-70.
[41]缪柏其,吴振翔,叶五一.基于Copula的外汇投资组合风险分析[J].中国管理科学. 2004, 12(4): 1-5.
[42]张世英,韦艳华.金融市场相关程度与相关模式的研究[J].系统工程学报. 2004, 19(4): 355-362.
[43]张世英,韦艳华.金融市场的相关性分析——Copula-GARCH模型及其应用[J].系统工程. 2004(4): 7-12.
[44]刘志东.基于Copula-GARCH-EVT的资产组合选择模型及其混合遗传算法[J].系统工程理论方法运用. 2006(2):32-37.
[45]史道济,李秀敏.金融市场组合风险的相关性研究[J].系统工程理论与实践. 2007(2):27-32.
[46] Bluhm C. CDO Modeling: Techniques, Examples and applications[R]. HypoVereinsbank, 2003.
[47] Elizalde A. Credit Risk Models II: Structural Models[R]. CEMFI Working Papers, 2005.
[48] Elizalde A. Reconciliatoin Reduced-Structural models[R]. CEMFI Working Papers, 2006.
[49] Giesecke K. Portfolio credit risk: top-down versus bottom-up approaches[M]. Frontiers in Quantitative Finance, Cont R, New Jersey:John Wiley & Sons, Inc, 2009, 251-268.
[50]秦学志,陈田.债务抵押债券(CDO)定价模型研究综述[J].管理学报. 2008, 5(4): 616-624.
[51] Andersen L, Sidenius J. CDO pricing with factor models: survey and comments[J]. Journal of Credit Risk. 2005, 1(3): 71-88.
[52] Laurent J, Cousin A. An overview of factor modeling for CDO pricing[M]. Frontiers in Quantitative Finance: Credit risk and Volatility modeling, Cont R, Hoboken:John Wiley & Sons, 2009.
[53] Alexander J M, Frey R, Embrechts P. Quantitative Risk Management: Concepts, Techniques and Tools[M]. New Jersey: Princeton University Press, 2005.
[54] Crouhy M, Galai D, Mark R. A comparative analysis of current redit risk models[J]. Journal of Banking and Finance. 2000, 24: 59-117.
[55] Finger C C. The one-factor CreditMetrics model in the New Basel Capital Accord[J]. RiskMetrics Journal. 2005, 2(1): 9-18.
[56] Greenberg A, Marshal R, Naldi M, et al. Turning correlation and tail risk to the market prices of liquid Tranches[J]. Quantitative Research Quarterly. 2004(7): 254-276.
[57] Gregory J, Laurent J. I will survive[J]. Risk. 2003(6): 103-107.
[58] Wison T. Portfolio Credit Risk 1[J]. Risk. 1997(9): 111-117.
[59] Wilson T. Portfolio Credit Risk 2[J]. Risk. 1997(10): 56-61.
[60] Frey R, Mcneil A. Dependent defaults in models of portfolio credit Risk[J]. Journal of Risk. 2003, 6(1): 59-92.
[61] Pykhtin M, Dev A. Credit risk in asset securitizations: Analytical model[J]. risk. 2002(5): 16-20.
[62] Li D. On default correlation: a copula approach[J]. Journal of Fixed Income. 2000(9): 43-54.
[63] Schonbucher P, Schubert D. Copula dependent default risk in intensity models[J]. working paper, University of Technology, Sydney. 2001.
[64] Vasicek O. Loan portfolio value[J]. Risk. 2002(12): 160-162.
[65] Cowell F, Racheva B, Truck S. Recent advances in credit risk management[M]. Risk Assessment Decisions in Banking and Finance, Bol G, Rachev S T, Wurth R, Heidelberg:Springer, 2009, 215-234.
[66] Andersen L, Sidenius J, Basu S. All your hedges in one basket[J]. Risk. 2003(11): 67-72.
[67] Mashal R, Naldi M, Zeevi A. Extreme events and multiname credit derivatives[M]. Credit Derivatives: The Definitive Guide, Gregory J, Landon:Risk Books, 2003, 313-338.
[68] Demarta S, Mcneil A. The t copula and related copulas[J]. International Statistical Review. 2005, 73(1): 111-129.
[69] Schloegl L, Okane D. A note on the large homogeneous portfolio approximation with the student t copula[J]. Finance and Stochastics. 2005, 9(4): 577-584.
[70] Hull J, White A. Valuation of a CDO and an nth to default CDS without Monte Carlo simulation[J]. Journal of Derivatives. 2004, 12(2): 8-23.
[71] Guegan D, Houdain J. Collateralized debt obligations pricing and factor models: a new methodology using normal inverse Gaussian distributions[R]. Working paper, ENS Cachan. 2005.
[72] Kalemanova A, Schmid B, Werner R. The normal inverse Gaussian distribution for synthetic CDO pricing[J]. New Jersey: Springer. 2007: 80-93.
[73] Moosbrucker T. Pricing CDOs with correlated variance gamma ditributions[R]. Working paper, Centre for Financial Research, University of Cologne. 2006.
[74] Albrecher H, Ladoucette S, Schoutens W. A generic one-factor Levy model for pricing synthetic CDOs[M]. Advances in Mathematical Finance, M F, R J, J Y, et al, Birkh?user, 2007, 259-278.
[75] Wang D, Rachev S, Fabozzi F. Pricing Tranches of CDO and a CDS index: recent advances and future research[M]. Risk Assessment: Decisions in Banking and Finance, Bol G, Rachev S, Wurth R, Heidelberg:Physica-Verlag, 2009, 263-286.
[76] Andersen L, Sidenius J. Extensions to the Gaussian copula: random recovery and random factor loading[J]. Journal of Credit Risk. 2005, 1(1): 29-70.
[77] Laurent J, Gregory J. Basket default swaps, CDOs and factor copulas[J]. Journal of Risk. 2005, 7(4): 103-122.
[78] Schonbucher P, Schubert D. copula dependent default risk in intensity models[R]. Working paper, Bonn University. 2001.
[79] Madan D, Konikov M, Marinescu M. Credit and basket default swaps[R]. Working paper, Bloomberg LP. 2004.
[80] Friend A, Rogge E. Correlation at first sight[J]. Economic Notes. 2005, 34(2): 155-183.
[81] Rogger E, Schonbucher P. Modelling dynamic portfolio credit risk[R]. working paper, Imperial College. 2003.
[82] Jackson K, Kreinin A, Zhang W. Dynamic factor copula model[R]. working paper, University of Toronto. 2009.
[83] Andersen L. Portfolio losses in factor models: term structures and intertemporal loss dependence. working paper. 2006.
[84] Sidenius J. On the term structure of loss distributions: a forward model approach[J]. International Journal of Theoretical and Applied Finance. 2007, 10(4): 749-761.
[85] Rogge E, Schonbucher P. Modelling dynamic portfolio credit risk[R]. working paper, ETH Zurich. 2003.
[86] Hull J, White A. Dynamic models of portfolio credit risk: a simplified approach[J]. Journal of Derivatives. 2008, 15(4): 9-28.
[87] Black F, Cox J. Valuing corporate securities: some effects of bond indenture provisions[J]. Journal of Finance. 1976, 31(2): 351-367.
[88] Arvanitis A, Gregory J. Credit: the complete guide to pricing, hedging and risk management[M]. London: Risk Books, 2004.
[89] Hull J, Predescu M, White A. The valuation of Correlation-dependent credit derivatives using a structural model[R]. working paper, University of Toronto. 2005.
[90] Willeman S. Fitting the CDO correlation skew: a tractable structural jump-diffusion model[J]. Journal of Credit Risk. 2007, 3(1): 63-90.
[91] Kiesel R, Scherer M. Dynamic credit portfolio modeling in structural models with jumps[R]. working paper, Ulm University and London School of Economics. 2007.
[92] Luciano E, Schoutens W. A multivariate jump-driven financial asset model[J]. Quantitative Finance. 2006, 6(5): 385-402.
[93] Baxter M. Gamma process dynamic modeling of credit[J]. Risk. 2007(10): 98-101.
[94] Lindskog F, Mcneil A. Common Poison shock models: applications to insurance and credit risk modeling[J]. ASTIN Bulletin. 2003, 33(2): 209-238.
[95] Duffie D, Singleton K. Simulating correlated defaults[R]. working paper, Stanford University. 1998.
[96] Wong D. Copula from the limit of multivariate binary model[R]. working paper, Bank of America Corporation. 2000.
[97] Giesecke K. A simple exponential model for dependent defaults[J]. Journal of Fixed Income. 2003(12): 74-83.
[98] Brigo D, Pallavicini A, Torresetti. CDO calibration with the dynamic generalized Poisson loss model[J]. Risk. 2007(5): 70-75.
[99] Brigo D, Pallavicini A, Torresetti R. Cluster-based extension of the generalized Poisson loss dynamics and consistency with single names[J]. Internatonal Journal of Theoretical and Applied Finance. 2007, 10(4): 607-631.
[100] Duffie D, G Arleanu N. Risk and the valuation of collateralized debt obligations[J]. Financial Analyst Journal. 2001, 57: 51-59.
[101] Mortensen A. Semi-analytical valuation of basket credit derivatives in intensity-based models[J]. Journal of Derivatives. 2006, 13(4): 8-26.
[102] Eckner A. Computational techniques for basic affine models of portfolio credit risk[R]. Working paper, Stanford University. 2007.
[103] Feldhutter P. An empirical investigation of an intensity-based model for pricing CDO tranches[R]. working paper, Copenhagen Business School. 2007.
[104] Fender I, Tarashev N, Zhu H. Credit fundamentals, ratings and value-at-risk: CDOs versus corporate exposures[J]. BIS Quarterly Review. 2008(March): 87-101.
[105] Krahnen J P, Wilde C. Risk Transfer with CDOs and Systemic Risk in banking[R]. working paper, Center for Financial Studies. 2006.
[106] Papadopoulos P, Tan C. Credit risk analysis of cashflow CDO structures[R]. ABN AMRO Bank, Group Risk Management, Models and Tools department, 2007.
[107] Hull J, White A. The Risk of Tranches Created from Residential Mortgages[R]. working paper, University of Toronto. 2009.
[108] Giesecke K, Kim B. Risk Analysis of Collateralized Debt Obligations[R]. working paper, Stanford University. 2009.
[109] Chavez-Demoulin V, Davisony A C, Mcneil A J. Estimating Value-at-Risk: A point process approach[R]. working paper, ETH-Zentrum. 2004.
[110]罗俊江.合成抵押债务契约(Synthetic CDO)的结构分析[R].深圳:深圳市平安期货经纪有限公司, 2007.
[111]张磊. CDO定价初探及我国CDO市场投资机会[R].上海:上海申银万国证券研究所有限公司, 2008.
[112]黄晔,莫桂青.信用衍生产品、合成CDOs与信用风险转移探讨[J].北方经济. 2008(2): 15-17.
[113]穆放,宋洁,陈治津.债务抵押债券定价模型探讨及实证研究[J].经济纵横. 2007(10): 10-13.
[114]冯谦,杨朝军.担保债权凭证定价——Copula函数的非参数估计与应用[J].运筹与管理. 2006, 15(5): 104-107.
[115]刘冬.抵押债务证券(CDO )的信用风险分析[D].成都:西南财经大学, 2007.
[116]李畅.结构性金融衍生产品定价研究[D].上海:同济大学, 2007.
[117] Cgfs. The Role of Ratings in Structured Finance: Issues and Implications[R]. Committee on the Global Financial System,Bank for International Settlements, 2005.
[118] Silver A A. Rating Structured Securities[M]. Issuer Perspectives on Securitization, Fabozzi F J, Hoboken:John Wiley & Sons, 1998.
[119] Jobst A A. What is structured finance? [J]. The Securitization Conduit. 2006, 8: 1-9.
[120]刘莉亚,邵斌.结构化金融产品[M].上海:上海财经大学出版社, 2005: 1-8.
[121] Das S. Structured Products & Hybrid Securities[M]. Asia: John Wiley & Sons, 2001.
[122] Cherubini U, Lunga G D. Structured Finance: The Object Oriented Approach[M].Atrium: John Wiley & Sons Ltd, 2007.
[123] Fabozzi F J, Davis H A, Choudhry M. Introduction to Structured Finance[M]. Hoboken: John Wiley & Sons, Inc., 2006.
[124] Tavakoli J M. Collateralized Debt Obligations and Structured Finance[M]. New Jersey: John Wiley & Sons, Inc, 2003: 27-29.
[125] Lucas D J, Goodman L S, Fabozzi F J. Collateralized Debt Obligations: Structures and Analysis[M]. New Jersey: John Wiley & Sons, Inc., 2006: 1-15.
[126] Davidson Andrew.资产证券化:构建和投资分析[M].北京:中国人民出版社, 2006.
[127] Maseck Oldrich,Choudhry Moorad.结构化的信用:现金流与复合型抵押债务债券[M].欧洲结构金融产品手册, Fabozzi Frank J.,Choudhry Moorad,北京:中国金融出版社, 2006, 481-520.
[128] Dullmann K, Kull J, Kunisch M. Estimating asset correlations from stock prices or default rates: Which method is superrior[R]. Frankfurt: Deutsche Bundesbank, 2008.
[129] Zeng B, Zhang J. An Empirical Assessment of Asset Correlation Models[R]. San Francisco: KMV LLC, 2001.
[130] Maglin P. Understanding default probabilities, default correlation, equity correlation, and Value at Risk: 150 commercial banks in U.S.[D]. San Francisco : Golden Gate University, 2008.
[131]张世英,韦艳华. Copula理论极其在金融分析上的应用[M].北京:清华大学出版社, 2008.
[132] Demarta S, Mcneil A. the t copula and related copulas[J]. International Statistical Review. 2005, 73(1): 111-129.
[133] Meneguzzo D, Vecchiato W. Copula sensitivity in collateralized debt obligations and basket default swaps[J]. Journal of Futures Markets. 2003, 24(1): 37-70.
[134] Rosenberg J V, Schuermann T. A general approach to integrated risk management with skewed, fat-tailed risks[J]. Journal of Financial Economics. 2006, 79(3): 569-614.
[135] Joe H. Multivariate Models and Dependence Concepts [M]. London: Chapman & Hall/CRC , 1997.
[136] Cont R. Empirical properties of asset returns: stylized facts and statistical issues[J].Quantitative Finance. 2001, 1: 223-236.
[137] Shephard N. Statistical aspects of ARCH and stochastic volatility[M]. Time Series Models, Cox D R, Hinkley D V , London:Chapman & Hall, 1996.
[138] Pagan A. The econometrics of financial markets[J]. Empirical Finance. 1996(3): 15-102.
[139] Rao, Maddala. Handbook of Statistics: Statistical Methods in Finance[M]. Amsterdam: North-Holland, 1997.
[140] Campbell J, Lo A H, Mckinlay C. The Econometrics of Financial Markets[M]. Princeton, NJ: Princeton University Press, 1997.
[141] Rydberg T H. Realistic statistical modeling of financial data[J]. International Statistical Review. 2000, 68(3): 233-258.
[142] Taylor S. Modeling Financial Time Series[M]. Chichester: John Wiley & Sons, 1986.
[143] Guillaume D M, Dacorogna M M, Dave R D, et al. From bird's eye to the microscope: a survey of new stylized facts of the intra-daily foreign exchange markets[J]. Finance and Stochastics. 1997(1): 95-129.
[144] Ghysels E, Gourieroux C, Jasiak J. High frequency financial time series data: some stylized facts and models of stochastic volatility[M]. In Nonlinear Modeling of High Frequency Financial Time Series, Financial Econometrics and Quantitative Analysis, Dunis C, Zhou B, Chichester:John Wiley & Sons, 1998, 127-159.
[145] Eberlein E, Keller U. Hyperbolic distributions in finance[J]. Bernoulli. 1995(1): 281-299.
[146] Behr A, Potter U. Alternatives to the normal model of stock returns: Gaussian mixture, generalized logF and generalized hyperbolic models[J]. Annals of Finance. 2009, 5: 49-68.
[147] Bond S. A Review of Asymmetric Conditional Density Functions in Autoregressive Conditional Heteroskedasticity Models[M]. Distributional Modelling in Finance, Knight J S S, Butterworth and Heinemann:Oxford press, 2000.
[148] Bollerslev T, Chou R Y, Kroner K F. ARCH modeling in Finance[J]. Journal of Econometrics. 1992, 52: 5-59.
[149] Bollerslev T, Engle R F, Nelson D B. ARCH models[M]. In Handbook of Econometrics, Engle R F, Mcfadden D L, Amsterdam:Elsevier Science, 1994.
[150] Andersen T G, Bollerslev T. Towards a unified framework for high and low frequency return volatility modeling[J]. statistica Neerlandica. 1998, 52: 273-302.
[151] Engle R F. Autoregressive Conditional Heteroskedasticity with Estimation of the Variance of United Kingdom Inflation[J]. Econometrica. 1982, 50: 987-1007.
[152] Bollerslev T. Generalized autoregressive conditional heteroskedasticity[J]. Journal of Econometrics. 1986(31): 307-327.
[153] Clark P K. A subordinated stochastic process model with finite variance for speculative prices[J]. Econometrica. 1973(41): 135-155.
[154] Black F. Studies of stock market volatility changes[M]. In Proceedings of American Statistical Association, Business and Economic Statistics Section, 1976, 177-181.
[155] Pagan A R, Schwert G W. Alternative models for conditional stock volatility[J]. Journal of Econometrics. 1990, 45: 267-290.
[156] Engle R F, Ng V K. Measuring and testing the impact of news on volatility[J]. Journal of Finance. 1993, 48: 1749-1778.
[157] Simkowitz M A, Beedles W L. Asymmetric stable distributed security returns[J]. Journal of the American Statistical Association. 1980, 75: 306-312.
[158] Kon S J. Models of stock returns: a comparison[J]. Journal of Finance. 1984, 39: 147-165.
[159] Singleton J C, Wingender J. Skewness persistence in common stock returns[J]. Journal of Financial and Quantitative Analysis. 1986, 21: 335-341.
[160] Theodossiou P. Financial data and the skewed generalized t distribution[J]. Management Science. 1998, 44: 1650-1661.
[161] Alles L A, Kling J L. Regularitis in the variation of skewness in asset returns[J]. Journal of Financial Research. 1994, 17: 427-438.
[162] Bekaert G, Erb C B, Harvey C R, et al. Distributional characteristics of emerging market returns and asset allocation[J]. Journal of Portfolio Management. 1998, 24: 102-116.
[163] Erb C V, Harvey C R, Viskanta T E. New perspectives on emerging market bonds[J]. Journal of Portfolio management. 1999, 25: 83-92.
[164] Li X, Xu Q. Evaluating density forecasts of the model with a conditional skewed-t distribution for China's stock markets[J]. Applied Financial Econometrics. 2008(18): 213-227.
[165] Liu H, Lee Y, Lee M. Forecasting China Stock Markets Volatility via GARCH Models under skewed GED distribution[J]. Journal of Money, Investment and Banking. 2009(7): 5-15.
[166] Harvey C R, Siddique A. Autoregressive Conditional Skewness[J]. The Journal of Financial and Quantitative Analysis. 1999, 34(4): 465-487.
[167] Hansen B E. Autoregressive Conditional Density Estimation[J]. International Economic Review. 1994, 35(3): 705-730.
[168] Harvey C R, Siddique A. Conditional skewness in asset pricing tests[J]. Journal of Finance. 2000, LV(3): 1263-1295.
[169] Wilhelmsson A. Value at Risk with time-varying variance, skewness and kurtosis - The NIG-ACD model[J]. The Econometrics Journal. 2009, 12: 82-104.
[170] Bollerslev T. A Conditional Heteroskedastic Time Series Model for Speculative Prices and Rates of Return[J]. Review of Economic Statistics. 1987, 69: 542-547.
[171] Nelson D. Conditional Heteroskedasticity in Asset Returns: A New Approach[J]. Econometrica. 1991, 59(2): 347-370.
[172] Fernandez C, Steel M. On Bayesian modelling of fat tails and skewness[J]. Journal of American Statistical Association. 1998, 93: 359-371.
[173] Barndorff-Nielsen O. Exponentially decreasing ditributions for the logarithm of particle size[C]. London: Royal Society London, 1977.
[174] Prause K. The generalized hyperbolic model: estimation, financial derivatives, and risk measures[D]. University of Freiburg, 1999.
[175] Protassov R. EM-based maximum likelihood parameter estimation for multivariate generalized hyperbolic distributions with fixed lambda[J]. Statistics and Computing. 2004, 14(1): 1-25.
[176] Protassov R. EM-based maximum likelihood parameter estimation for multivariate generalized hyperbolic distributions with fixed lammda[J]. Statistics and Computing. 2004, 14(1): 24-65.
[177] Forsberg L, Bollerslev T. Bridging the gap between the distribution of realized (ECU) volatility and GARCh modeling (of the EURO): the GARCH-NIG model[J]. Journal of Applied Econometrics. 2002(17): 535-548.
[178] Mcneil A J, Frey R, Embrechts P. Quantitative Risk Management: Concepts, Techniques and Tools[M]. New Jersey: Princeton University Press, 2005.
[179] Barndorff-Nielsen O E, Blasid P. Hyperbolic distributions and ramifications: contributions to theory and application[M]. In Statistical Distributions in Scientific Work, Taillie C, Patil G, Baldessari B, Dordrecht:Reidel, 1981: 4, 19-44.
[180] Bibby B, Sorensen M. Hyperbolic processes in finance[R]. University of Aarhus, Aarhus School of Bussiness, 2001.
[181] Chakravarti, Laha, Roy. Handbook of Methods of Applied Statistics[M]. John Wiley and Sons, 1967.
[182] Tavares A B, Curto J D, Tavares G N. Modeling Heavy tails and Asymmetry using ARCH-type Models with Stable Paretian Distributions[J]. Nonlinear Dynamics. 2008, 51: 231-243.
[183] Engle R F, Lilien D M, Robins R P. Estimating tiem varying risk premium in the term-structure: the ARCH-M model[J]. Econometrica. 1987, 55(2): 391-407.
[184] Hansen P, Lunde A. A forecast comparison of variance models: does anything beat a GARCH(1,1)?[J]. Journal of Applied Econometrics. 2005(20): 873-889.
[185] Poon S, Granger C. Forecasting variance in financial markets: a review[J]. Journal of Economic Literature. 2003(41): 478-540.
[186] Zivot E, Wang J. Modeling Financial Time Series with S-Plus[M]. Second Edition ed. Boston: Springer, 2005.
[187] Bollerslev T. A conditionally heteroskedastic time series model of security preces and rates of return data[J]. Review of Economics and Statistics. 1987, 59: 542-547.
[188] French K R, Schwert G W, Stambaugh R F. Expected stock returns and volatility[J]. Journal of Financial Economics. 1987, 19: 3-29.
[189] Schwert W. Stock Volatility and the Crash of S87[J]. Review of Financial Studies. 1990, 3(77-102).
[190] Nelson D B, Foster D B. Asymptotic filtering theory for univariate ARCH models[J]. Econometrica. 1994, 62: 1-41.
[191] Higgins M L, Bera A K. A Class of Nonlinear ARCH Models[J]. International Economic Review. 1992, 33: 137-158.
[192] Sentana E. Quadratic ARCH models: A potential reinterpretation of ARCH models as second-order Taylor approximation[R]. London: London School of Economics and Political Science. 1991.
[193] Zakoian J M. Threshold Heteroskedasticity Models[J]. Journal of Economic Dynamics and Control. 1994, 15: 931-955.
[194] Glosten L, Jagannathan R, Runkle D. On the Relation between Expected Value and the Volatility of the Nominal Excess Return on stocks[J]. Journal of Finance. 1993, 48: 1779-1801.
[195] Ding Z, Granger C W J, Engle R F. A Long Memory Property of Stock Market Returns and a New Model[J]. Journal of Empirical Finance. 1993, 1: 83-106.
[196] Barndorff-Nielsen O E. Normal Inverse Gaussian Distributions and Stochastic Volatility Modelling[J]. Scandinavian Journal of Statistics. 1997, 24(1): 1-13.
[197] Andersson J. On the Normal Inverse Gaussian Stochastic Volatility Model[J]. Journal of Business & Economic Statistics. 2001, 19(1): 44-54.
[198] Jensen M B, Lunde A. The NIG-S&ARCH model: a fat-tailed, stochastic, and autoregressive conditional heteroskedastic volatility model[J]. Econometrics Journal. 2001(4): 319-342.
[199] Jorion P. Value at Risk: the New Benchmark for Managing Financial Risk[M]. Third edition ed. Boston: The McGraw-Hill Companies, Inc, 2007.
[200] Artzner P, Delbaen R, Heath D. Thinking Coherently[J]. Risk. 1997, 10(11): 68-71.
[201] Philippe Artzner R D D H. Coherent Measures of Risk[J]. Mathematical Finance. 1999, 9(3): 203-228.
[202] Crouhy M, Galai D, Mark R. Prototype risk rating system[J]. Journal of Banking & Finance. 2001, 25: 41-95.
[203] Bluhm C, Overbeck L, Wagner C. An introduction to credit risk modeling[M]. London: Chapman & Hall/CRC, 2003.
[204] Saunders A, Allen L. Credit Risk Measurement: New Approaches to Value at Risk and Other Paradigms[M]. Second Edition ed. New York: John Wiley & Sons, 2002: 95-97.
[205] Altman E I, Kao D L. The Implications of Corporate Bond Ratings Drift[J]. Finanical Analysts Journal. 1992(5): 64-75.
[206] Nickell P, Perraudin W, Varotto S. Stability of Rating Transitions[J]. Journal of Banking and Finance. 2001, 24(1/2): 203-228.
[207] Altman E I, Kishore V M. Defaults and return on high yield bonds: analysis through 1997[R]. working paper, New York University Salomon Centre. 1998.
[208] Gupton M, Finger C C, Bhatia M. CreditMetrics-Technical Document[EB/OL]. New York: Morgan Guaranty Trust Company, 1997.
[209] Wilson T. Portfolio credit risk, part I[J]. Risk. 1997(9): 111-117.
[210] Wilson T. Portfolio credit risk, part II[J]. Risk. 1997(10): 56-61.
[211] Hu Y T, Kiesel G R, Perraudin W. The estimation of transition matrics for sovereign credit ratings. working paper. 2001.
[212] Credit Suisse Financial Products. CreditRisk+: A Credit Risk Management Framework[EB/OL]. Swiss: Credit Suisse Financial Products, 1997.
[213] Rice J A. Mathematical Statistics and Data Analysis[M]. second edition ed. Duxbury Press, 1995.
[214] Duffie D, Garleanu N. Risk and Valuation of Collateralized Debt Obligations[J]. Financial Analysts Journal. 2001(1): 41-62.
[215] Schonbucher P J. Factor Models: Portfolio Credit Risk When Defaults are Correlated [J]. The Journal of Risk Finance. 2001, 3(1): 45-56.
[216] Kealhofer S, Bohn J. Portfolio management of default risk[R]. KMV. http://www.kmv.com., 2001.
[217] Crosbie P, Bohn J. Modeling default risk[R]. KMV . http://www.kmv.com., 2002.
[218] Hu W. Calibration of Multivariate Generalized Hyperbolic Distributions using the EM Algorithm, with Applications in Risk Management, Portfolio Optimazation adn Portfolio Credit Risk[D]. Florida State University, 2005.
[219] Coval J D, Jurek J W, Stafford E. The Economics of Structured Finance[J]. Journal of Economic Perspectives. 2009, 23(1): 3-25.
[220] Bl?sid P, S?rensen M K. 'HYP'---A computer program for analyzing data by means of the hyperbolic distribution[R]. Department of Theoretical Statistics, University of Aarhus, Denmark, 1992.
[221] Prause K. The generalized hyperbolic model: estimation, financial derivatives, and riskmeasures[D]. University of Feiburg, 1999.
[222] Lillestol J. Risk analysis and the NIG distribution[J]. Journal of Risk. 2000(4): 41-55.
[223] Dempster A P, Laird N M, Rubin D. Maximum likelihood from incomplete data via the EM algorithm[J]. Journal of the Royal Statistical Society: Series B (Statistical Methodology). 1977, 39: 1-38.
[224] Liu C, Rubin D. ML estimation of the t distribution using EM and its extensions, ECM and ECME[J]. Statistica Sinica. 1995(5): 19-39.
[225] Abramowitz M, Stegum I A. Handbook of Mathematical Functions[M]. New York: Dover, 1974: 78-96.
[226]徐晓肆,任若恩.信用等级转移分析[J].生产力研究. 2004(9): 42-44.
[227]杨怀东,郭亚军,明东.信用转移矩阵预测模型的比较分析[J].东北大学学报(自然科学版). 2004, 25(5): 508-510.
[228]张玲,曾维火.基于Z值模型的我国上市公司信用评级研究[J].财经研究. 2004, 30(6): 5-13.
[229]张玲,赵峰.信用风险转移矩阵在可转债定价研究中的应用[J].金融教学与研究. 2005(3): 40-42.
[230] Parker G, Barber T, Benoit B. Financial stability: EU leaders take credit rating agencies to task[N]. Financial Times, 30 January, 2008.
[231] International Organization of Securities Commissions. Report on the subprime crisis[R]. New York: International Organization of Securities Commissions, 2008.
[232] Financial Services Authority. Financial Risk Outlook 2008[R]. Landon: Financial Services Authority, 2008.
[233] Committee of European Securities Regulators. The role of credit rating agencies in structured finance: Consultation paper[R]. Paris: Committee of European Securities Regulators, 2008.
[234] Chung J, Duyn V A. SEC to report on credit rating agencies[N]. Financial Times, 23 April, 2008.
[235] International Organization of Securities Commissions. Code of conduct fundamentals for credit rating agencies (revised)[R]. Madrid: International Organization of Securities Commissions, 2008.
[236] The Financial Stability Forum. Financial risk outlook 2008[R]. Landon: The Financial Stability Forum, 2008.
[237] Securities Industry and Financial Markets Association. Recommendations of the securities industry and financial markets association credit rating agencies task force[R]. New York: Securities Industry and Financial Markets Association, 2008.
[238] Fender I, Kiff J. CDO rating methodology: some thoughts on model risk and its implications[J]. Journal of Credit Risk. 2005(1): 37-58.
[239] Mork?tter S, Westerfeld S. Rating model arbitrage in CDO markets: an empirical analysis[J]. International Review of Financial Analysis, doi:10.1016/j.irfa.2009.01.002. 2009.
[240] Duff A, Einig S. Credit rating quality: the perceptions of market participants and other interested parties[J]. The British Accounting Review, doi:10.1016/j.bar.2008.11.003. 2009.
[241]王少波.资产证券化信用评级:国外模型方法及其借鉴[J].当代经济科学. 2007(6): 37-44.
[242]李扬.谈资产支持证券信用风险控制中的信用评级[J].财会月刊. 2008(2): 35-37.
[243]朱宸刚.信贷资产证券化-CDO产品信用评级方法对投资者保护的影响分析[J].金融与经济. 2008(2): 62-65.
[244]张明.透视CDO:类型、构造、评级与市场[J].国际金融研究. 2008(6): 40-48.
[245] Gluck J, Remeza H. Moody’s approach to rating multi-sector CDOs[R]. New York: Moody’s Investors Service Structured Finance Special Report, 2008.
[246] Witt G. Moody’s correlated binomial default distribution[R]. New York: Moody’s Investor Service Structured Finance Special Report, 2005.
[247] Peretyatkin V, Perraudin W. EL and DP approaches to rating CDO and the scope for‘ratings shopping’[M]. in Credit Ratings-methodologies, Rationale and Default Risk, Ong M K, London:Risk Books, 2002.
[248] National Economic Research Association. Credit ratings for structured products[R]. London: National Economic Research Association, 2003.
[249] Duffie D, Singleton J C. Credit Risk: Pricing, Measurement and Management[M]. New Jersey: Princeton University Press, 2003.
[250] Lando D. Credit Risk Modeling: Theory and Applications[M]. New Jersey: Princeton University Press, 2004.
[251] Ramaswamy S. Managing Credit Risk in Corporate Bond Portfolios[M]. New Jersey: John Wiley & Sons, Inc., 2004.
[252]王树同.抵押债务债券(CDO)风险收益特征及其在我国的发展前景[J].新金融. 2007(5): 46-50.
[2] Fama F. The behavior of stock prices[J]. Journal of Business. 1965(38): 34-105.
[3] Georgieva A. The use of structured products: applications, benefits and limitations for the institutional investor[R]. Zurich: University of Zurich, 2005.
[4] Hens T, Rieger M O. The Dark Side of the Moon: Structured Products from the Customer’s Perspective[J]. working paper, Zurich: University Zurich. 2009.
[5] Rajan A, Mcdermott G, Roy R. The Structured Credit Handbook[M]. New York: John Wiley & Sons Inc, 2007: 1-11.
[6]法博兹弗兰克J.,休亨瑞莫德.欧洲结构金融产品手册[M].北京:中国金融出版社, 2006.
[7] Blundell-Wignall A. Structured Products: Implications for Financial Markets[J]. Financial Market Trends. 2007(2): 1-93.
[8] Whetten M, Adelson M. CDOs-squared Demystified[R]. Nomura Fixed Income Research, 2007.
[9] Lucas D J, Goodman L S, Fabozzi F J. Collateralized Debt Obligations and Credit Risk Transfer[R]. New Haven: Yale University, 2007.
[10] Wilde J P K A. Risk Transfer with CDOs[R]. Frankfurt: Frankfurt University's Center for Financial Studies, 2008.
[11] Deventer D R. CDOs and the Credit Crisis Complexity and model risk in the collateralized debt obligation market are severe[R]. Hawaii: Kamakura Corporation, 2008.
[12] Hull J C. The Credit Crunch of 2007: What Went Wrong? Why? What Lessons Can Be Learned[J]. Journal of Credit Risk. 2009, 5(2): 3-18.
[13] Crouhy M G, Jarrow R A, Turnbull S M. The Subprime Credit Crisis of 07[J]. Journal of Derivatives. 2008, 16(1): 81-111.
[14] Merton R C. On the pricing of corporate debt: the risk structure of interest rates[J]. Journal of Finance. 1974, 29: 449-470.
[15] Kim J, Ramaswamy K, Sundaresan S. Does Default Risk in Coupons Affect theValuation of Corporate Bonds? A Contingent Claims Model[J]. Financial Management. 1993, 22: 117-131.
[16] Longstaff F, Schwartz E. A Simple Approach to Value Risky Fixed and Floating Rate Debt[J]. Journal of Finance. 1995, 50: 789-819.
[17] Mason S, Bhattacharya S. Risk Debt, Jump Process, and Safety Covenants[J]. Journal of Financial Economics. 1981(9): 281-307.
[18] Sch?nbucher P. The Term Structure of Defaultable Bond Prices[J]. Review of Derivatives Research. 1998(6): 161-192.
[19] Leland H. Corporate Debt Value, Bond Covenants, and Optimal Structure[J]. Journal of Finance. 1994, 49: 1213-1252.
[20] Uhrig M, Walter U. A New Numerical Approach for Fitting the Initial Yield Curve[J]. Journal of Fixed Income. 1996(5): 82-90.
[21] Anderson R, Sundaresan S. Design and Valuation of Debt Contracts[J]. Review of Financial Studies. 1996(9): 37-68.
[22] Fan H, Sundaresan S. Debt Valuation, Strategic Debt Service and Optimal Dividend Policy [J]. Review of Financial Studies. 2000, 13(1057-1099).
[23] Jarrow R, Turnbull S. Pricing Derivatives on Financial Securities Subject to Credit Risk[J]. Journal of Finance. 1995, 50: 53-85.
[24] Jarrow R, Lando D, Turnbull S. A Markov Model for the Term Structure of Credit Risk Spreads[J]. Review of Financial Studies. 1997(10): 481-523.
[25] Madan D, Unal H. Pricing the Risk of Default[J]. Review of Derivatives Research. 1998(2): 121-160.
[26] Lando D. On Cox Processes and Credit Risky Securities[J]. Review of Derivatives Research. 1998(2): 99-120.
[27] Duffie D, Singleton K. Modeling Term Structure of Default Risk Bonds[J]. Review of Financial Studies. 1999(12): 687-720.
[28] Abid F, Naifar N. CDO Parameters Estimation Using Market Prices[J]. International Research Journal of Finance and Economics. 2008(18): 85-95.
[29] Hrvatin R, Neugebauer M. Default correlation and its effect on portfolio of credit risk[R]. New York: FitchRatings, 2004.
[30] Lee M, Beinstein E, Ahluwalia R, et al. Credit correlation: a guide[R]. London: JPMorgan, Credit Derivatives Strategy, 2004.
[31] Nelsen R B. An Introduction to Copulas[M]. Second Edition ed. New York: Springer, 2006.
[32] Embrechts P, Mcneil A, Straumann D. Correlation: Pitfalls and Alternatives[J]. Risk. 1999(12): 69-71.
[33] Embrechts P, Lindskog F, Mcneil A. Modeling Dependence with Copulas and Applications to Risk Management[M]. Handbook of Heavy Tailed Distributions in Finance, Rachev S, New Jersy: Elsevier, 2003, 329-384.
[34] Patton A. modeling Asymmetric Exchange Rate Dependence[J]. International Economic Review. 2006, 47(2): 527-556.
[35] Cherubini U, Luciano E, Vecchiato W. Copula Methods in Finance[M]. England: John Wiley & Sons Ltd, 2004.
[36] Genest C, Ghoudi K, Rives L P. A Semiparametric Estimation Procedure of Dependence Parameters in Multivariate Families of Distribution[J]. Biometrika. 1995, 82: 543-552.
[37] Embrechts P. Copulas: A personal view[J]. Journal of Risk and Insurance. 76, 639-650.
[38] Clemente D A, Romano C. Measuring Portfolio Value-at-risk by Copula-EVT based Approach. working paper. 2003.
[39]张尧庭.连接函数(Copola)技术与金融风险分析[J].统计研究. 2002(4): 48-51.
[40]张明恒.多金融资产风险价值的Copula计量方法研究[J].数量经济技术经济研究. 2004(4): 67-70.
[41]缪柏其,吴振翔,叶五一.基于Copula的外汇投资组合风险分析[J].中国管理科学. 2004, 12(4): 1-5.
[42]张世英,韦艳华.金融市场相关程度与相关模式的研究[J].系统工程学报. 2004, 19(4): 355-362.
[43]张世英,韦艳华.金融市场的相关性分析——Copula-GARCH模型及其应用[J].系统工程. 2004(4): 7-12.
[44]刘志东.基于Copula-GARCH-EVT的资产组合选择模型及其混合遗传算法[J].系统工程理论方法运用. 2006(2):32-37.
[45]史道济,李秀敏.金融市场组合风险的相关性研究[J].系统工程理论与实践. 2007(2):27-32.
[46] Bluhm C. CDO Modeling: Techniques, Examples and applications[R]. HypoVereinsbank, 2003.
[47] Elizalde A. Credit Risk Models II: Structural Models[R]. CEMFI Working Papers, 2005.
[48] Elizalde A. Reconciliatoin Reduced-Structural models[R]. CEMFI Working Papers, 2006.
[49] Giesecke K. Portfolio credit risk: top-down versus bottom-up approaches[M]. Frontiers in Quantitative Finance, Cont R, New Jersey:John Wiley & Sons, Inc, 2009, 251-268.
[50]秦学志,陈田.债务抵押债券(CDO)定价模型研究综述[J].管理学报. 2008, 5(4): 616-624.
[51] Andersen L, Sidenius J. CDO pricing with factor models: survey and comments[J]. Journal of Credit Risk. 2005, 1(3): 71-88.
[52] Laurent J, Cousin A. An overview of factor modeling for CDO pricing[M]. Frontiers in Quantitative Finance: Credit risk and Volatility modeling, Cont R, Hoboken:John Wiley & Sons, 2009.
[53] Alexander J M, Frey R, Embrechts P. Quantitative Risk Management: Concepts, Techniques and Tools[M]. New Jersey: Princeton University Press, 2005.
[54] Crouhy M, Galai D, Mark R. A comparative analysis of current redit risk models[J]. Journal of Banking and Finance. 2000, 24: 59-117.
[55] Finger C C. The one-factor CreditMetrics model in the New Basel Capital Accord[J]. RiskMetrics Journal. 2005, 2(1): 9-18.
[56] Greenberg A, Marshal R, Naldi M, et al. Turning correlation and tail risk to the market prices of liquid Tranches[J]. Quantitative Research Quarterly. 2004(7): 254-276.
[57] Gregory J, Laurent J. I will survive[J]. Risk. 2003(6): 103-107.
[58] Wison T. Portfolio Credit Risk 1[J]. Risk. 1997(9): 111-117.
[59] Wilson T. Portfolio Credit Risk 2[J]. Risk. 1997(10): 56-61.
[60] Frey R, Mcneil A. Dependent defaults in models of portfolio credit Risk[J]. Journal of Risk. 2003, 6(1): 59-92.
[61] Pykhtin M, Dev A. Credit risk in asset securitizations: Analytical model[J]. risk. 2002(5): 16-20.
[62] Li D. On default correlation: a copula approach[J]. Journal of Fixed Income. 2000(9): 43-54.
[63] Schonbucher P, Schubert D. Copula dependent default risk in intensity models[J]. working paper, University of Technology, Sydney. 2001.
[64] Vasicek O. Loan portfolio value[J]. Risk. 2002(12): 160-162.
[65] Cowell F, Racheva B, Truck S. Recent advances in credit risk management[M]. Risk Assessment Decisions in Banking and Finance, Bol G, Rachev S T, Wurth R, Heidelberg:Springer, 2009, 215-234.
[66] Andersen L, Sidenius J, Basu S. All your hedges in one basket[J]. Risk. 2003(11): 67-72.
[67] Mashal R, Naldi M, Zeevi A. Extreme events and multiname credit derivatives[M]. Credit Derivatives: The Definitive Guide, Gregory J, Landon:Risk Books, 2003, 313-338.
[68] Demarta S, Mcneil A. The t copula and related copulas[J]. International Statistical Review. 2005, 73(1): 111-129.
[69] Schloegl L, Okane D. A note on the large homogeneous portfolio approximation with the student t copula[J]. Finance and Stochastics. 2005, 9(4): 577-584.
[70] Hull J, White A. Valuation of a CDO and an nth to default CDS without Monte Carlo simulation[J]. Journal of Derivatives. 2004, 12(2): 8-23.
[71] Guegan D, Houdain J. Collateralized debt obligations pricing and factor models: a new methodology using normal inverse Gaussian distributions[R]. Working paper, ENS Cachan. 2005.
[72] Kalemanova A, Schmid B, Werner R. The normal inverse Gaussian distribution for synthetic CDO pricing[J]. New Jersey: Springer. 2007: 80-93.
[73] Moosbrucker T. Pricing CDOs with correlated variance gamma ditributions[R]. Working paper, Centre for Financial Research, University of Cologne. 2006.
[74] Albrecher H, Ladoucette S, Schoutens W. A generic one-factor Levy model for pricing synthetic CDOs[M]. Advances in Mathematical Finance, M F, R J, J Y, et al, Birkh?user, 2007, 259-278.
[75] Wang D, Rachev S, Fabozzi F. Pricing Tranches of CDO and a CDS index: recent advances and future research[M]. Risk Assessment: Decisions in Banking and Finance, Bol G, Rachev S, Wurth R, Heidelberg:Physica-Verlag, 2009, 263-286.
[76] Andersen L, Sidenius J. Extensions to the Gaussian copula: random recovery and random factor loading[J]. Journal of Credit Risk. 2005, 1(1): 29-70.
[77] Laurent J, Gregory J. Basket default swaps, CDOs and factor copulas[J]. Journal of Risk. 2005, 7(4): 103-122.
[78] Schonbucher P, Schubert D. copula dependent default risk in intensity models[R]. Working paper, Bonn University. 2001.
[79] Madan D, Konikov M, Marinescu M. Credit and basket default swaps[R]. Working paper, Bloomberg LP. 2004.
[80] Friend A, Rogge E. Correlation at first sight[J]. Economic Notes. 2005, 34(2): 155-183.
[81] Rogger E, Schonbucher P. Modelling dynamic portfolio credit risk[R]. working paper, Imperial College. 2003.
[82] Jackson K, Kreinin A, Zhang W. Dynamic factor copula model[R]. working paper, University of Toronto. 2009.
[83] Andersen L. Portfolio losses in factor models: term structures and intertemporal loss dependence. working paper. 2006.
[84] Sidenius J. On the term structure of loss distributions: a forward model approach[J]. International Journal of Theoretical and Applied Finance. 2007, 10(4): 749-761.
[85] Rogge E, Schonbucher P. Modelling dynamic portfolio credit risk[R]. working paper, ETH Zurich. 2003.
[86] Hull J, White A. Dynamic models of portfolio credit risk: a simplified approach[J]. Journal of Derivatives. 2008, 15(4): 9-28.
[87] Black F, Cox J. Valuing corporate securities: some effects of bond indenture provisions[J]. Journal of Finance. 1976, 31(2): 351-367.
[88] Arvanitis A, Gregory J. Credit: the complete guide to pricing, hedging and risk management[M]. London: Risk Books, 2004.
[89] Hull J, Predescu M, White A. The valuation of Correlation-dependent credit derivatives using a structural model[R]. working paper, University of Toronto. 2005.
[90] Willeman S. Fitting the CDO correlation skew: a tractable structural jump-diffusion model[J]. Journal of Credit Risk. 2007, 3(1): 63-90.
[91] Kiesel R, Scherer M. Dynamic credit portfolio modeling in structural models with jumps[R]. working paper, Ulm University and London School of Economics. 2007.
[92] Luciano E, Schoutens W. A multivariate jump-driven financial asset model[J]. Quantitative Finance. 2006, 6(5): 385-402.
[93] Baxter M. Gamma process dynamic modeling of credit[J]. Risk. 2007(10): 98-101.
[94] Lindskog F, Mcneil A. Common Poison shock models: applications to insurance and credit risk modeling[J]. ASTIN Bulletin. 2003, 33(2): 209-238.
[95] Duffie D, Singleton K. Simulating correlated defaults[R]. working paper, Stanford University. 1998.
[96] Wong D. Copula from the limit of multivariate binary model[R]. working paper, Bank of America Corporation. 2000.
[97] Giesecke K. A simple exponential model for dependent defaults[J]. Journal of Fixed Income. 2003(12): 74-83.
[98] Brigo D, Pallavicini A, Torresetti. CDO calibration with the dynamic generalized Poisson loss model[J]. Risk. 2007(5): 70-75.
[99] Brigo D, Pallavicini A, Torresetti R. Cluster-based extension of the generalized Poisson loss dynamics and consistency with single names[J]. Internatonal Journal of Theoretical and Applied Finance. 2007, 10(4): 607-631.
[100] Duffie D, G Arleanu N. Risk and the valuation of collateralized debt obligations[J]. Financial Analyst Journal. 2001, 57: 51-59.
[101] Mortensen A. Semi-analytical valuation of basket credit derivatives in intensity-based models[J]. Journal of Derivatives. 2006, 13(4): 8-26.
[102] Eckner A. Computational techniques for basic affine models of portfolio credit risk[R]. Working paper, Stanford University. 2007.
[103] Feldhutter P. An empirical investigation of an intensity-based model for pricing CDO tranches[R]. working paper, Copenhagen Business School. 2007.
[104] Fender I, Tarashev N, Zhu H. Credit fundamentals, ratings and value-at-risk: CDOs versus corporate exposures[J]. BIS Quarterly Review. 2008(March): 87-101.
[105] Krahnen J P, Wilde C. Risk Transfer with CDOs and Systemic Risk in banking[R]. working paper, Center for Financial Studies. 2006.
[106] Papadopoulos P, Tan C. Credit risk analysis of cashflow CDO structures[R]. ABN AMRO Bank, Group Risk Management, Models and Tools department, 2007.
[107] Hull J, White A. The Risk of Tranches Created from Residential Mortgages[R]. working paper, University of Toronto. 2009.
[108] Giesecke K, Kim B. Risk Analysis of Collateralized Debt Obligations[R]. working paper, Stanford University. 2009.
[109] Chavez-Demoulin V, Davisony A C, Mcneil A J. Estimating Value-at-Risk: A point process approach[R]. working paper, ETH-Zentrum. 2004.
[110]罗俊江.合成抵押债务契约(Synthetic CDO)的结构分析[R].深圳:深圳市平安期货经纪有限公司, 2007.
[111]张磊. CDO定价初探及我国CDO市场投资机会[R].上海:上海申银万国证券研究所有限公司, 2008.
[112]黄晔,莫桂青.信用衍生产品、合成CDOs与信用风险转移探讨[J].北方经济. 2008(2): 15-17.
[113]穆放,宋洁,陈治津.债务抵押债券定价模型探讨及实证研究[J].经济纵横. 2007(10): 10-13.
[114]冯谦,杨朝军.担保债权凭证定价——Copula函数的非参数估计与应用[J].运筹与管理. 2006, 15(5): 104-107.
[115]刘冬.抵押债务证券(CDO )的信用风险分析[D].成都:西南财经大学, 2007.
[116]李畅.结构性金融衍生产品定价研究[D].上海:同济大学, 2007.
[117] Cgfs. The Role of Ratings in Structured Finance: Issues and Implications[R]. Committee on the Global Financial System,Bank for International Settlements, 2005.
[118] Silver A A. Rating Structured Securities[M]. Issuer Perspectives on Securitization, Fabozzi F J, Hoboken:John Wiley & Sons, 1998.
[119] Jobst A A. What is structured finance? [J]. The Securitization Conduit. 2006, 8: 1-9.
[120]刘莉亚,邵斌.结构化金融产品[M].上海:上海财经大学出版社, 2005: 1-8.
[121] Das S. Structured Products & Hybrid Securities[M]. Asia: John Wiley & Sons, 2001.
[122] Cherubini U, Lunga G D. Structured Finance: The Object Oriented Approach[M].Atrium: John Wiley & Sons Ltd, 2007.
[123] Fabozzi F J, Davis H A, Choudhry M. Introduction to Structured Finance[M]. Hoboken: John Wiley & Sons, Inc., 2006.
[124] Tavakoli J M. Collateralized Debt Obligations and Structured Finance[M]. New Jersey: John Wiley & Sons, Inc, 2003: 27-29.
[125] Lucas D J, Goodman L S, Fabozzi F J. Collateralized Debt Obligations: Structures and Analysis[M]. New Jersey: John Wiley & Sons, Inc., 2006: 1-15.
[126] Davidson Andrew.资产证券化:构建和投资分析[M].北京:中国人民出版社, 2006.
[127] Maseck Oldrich,Choudhry Moorad.结构化的信用:现金流与复合型抵押债务债券[M].欧洲结构金融产品手册, Fabozzi Frank J.,Choudhry Moorad,北京:中国金融出版社, 2006, 481-520.
[128] Dullmann K, Kull J, Kunisch M. Estimating asset correlations from stock prices or default rates: Which method is superrior[R]. Frankfurt: Deutsche Bundesbank, 2008.
[129] Zeng B, Zhang J. An Empirical Assessment of Asset Correlation Models[R]. San Francisco: KMV LLC, 2001.
[130] Maglin P. Understanding default probabilities, default correlation, equity correlation, and Value at Risk: 150 commercial banks in U.S.[D]. San Francisco : Golden Gate University, 2008.
[131]张世英,韦艳华. Copula理论极其在金融分析上的应用[M].北京:清华大学出版社, 2008.
[132] Demarta S, Mcneil A. the t copula and related copulas[J]. International Statistical Review. 2005, 73(1): 111-129.
[133] Meneguzzo D, Vecchiato W. Copula sensitivity in collateralized debt obligations and basket default swaps[J]. Journal of Futures Markets. 2003, 24(1): 37-70.
[134] Rosenberg J V, Schuermann T. A general approach to integrated risk management with skewed, fat-tailed risks[J]. Journal of Financial Economics. 2006, 79(3): 569-614.
[135] Joe H. Multivariate Models and Dependence Concepts [M]. London: Chapman & Hall/CRC , 1997.
[136] Cont R. Empirical properties of asset returns: stylized facts and statistical issues[J].Quantitative Finance. 2001, 1: 223-236.
[137] Shephard N. Statistical aspects of ARCH and stochastic volatility[M]. Time Series Models, Cox D R, Hinkley D V , London:Chapman & Hall, 1996.
[138] Pagan A. The econometrics of financial markets[J]. Empirical Finance. 1996(3): 15-102.
[139] Rao, Maddala. Handbook of Statistics: Statistical Methods in Finance[M]. Amsterdam: North-Holland, 1997.
[140] Campbell J, Lo A H, Mckinlay C. The Econometrics of Financial Markets[M]. Princeton, NJ: Princeton University Press, 1997.
[141] Rydberg T H. Realistic statistical modeling of financial data[J]. International Statistical Review. 2000, 68(3): 233-258.
[142] Taylor S. Modeling Financial Time Series[M]. Chichester: John Wiley & Sons, 1986.
[143] Guillaume D M, Dacorogna M M, Dave R D, et al. From bird's eye to the microscope: a survey of new stylized facts of the intra-daily foreign exchange markets[J]. Finance and Stochastics. 1997(1): 95-129.
[144] Ghysels E, Gourieroux C, Jasiak J. High frequency financial time series data: some stylized facts and models of stochastic volatility[M]. In Nonlinear Modeling of High Frequency Financial Time Series, Financial Econometrics and Quantitative Analysis, Dunis C, Zhou B, Chichester:John Wiley & Sons, 1998, 127-159.
[145] Eberlein E, Keller U. Hyperbolic distributions in finance[J]. Bernoulli. 1995(1): 281-299.
[146] Behr A, Potter U. Alternatives to the normal model of stock returns: Gaussian mixture, generalized logF and generalized hyperbolic models[J]. Annals of Finance. 2009, 5: 49-68.
[147] Bond S. A Review of Asymmetric Conditional Density Functions in Autoregressive Conditional Heteroskedasticity Models[M]. Distributional Modelling in Finance, Knight J S S, Butterworth and Heinemann:Oxford press, 2000.
[148] Bollerslev T, Chou R Y, Kroner K F. ARCH modeling in Finance[J]. Journal of Econometrics. 1992, 52: 5-59.
[149] Bollerslev T, Engle R F, Nelson D B. ARCH models[M]. In Handbook of Econometrics, Engle R F, Mcfadden D L, Amsterdam:Elsevier Science, 1994.
[150] Andersen T G, Bollerslev T. Towards a unified framework for high and low frequency return volatility modeling[J]. statistica Neerlandica. 1998, 52: 273-302.
[151] Engle R F. Autoregressive Conditional Heteroskedasticity with Estimation of the Variance of United Kingdom Inflation[J]. Econometrica. 1982, 50: 987-1007.
[152] Bollerslev T. Generalized autoregressive conditional heteroskedasticity[J]. Journal of Econometrics. 1986(31): 307-327.
[153] Clark P K. A subordinated stochastic process model with finite variance for speculative prices[J]. Econometrica. 1973(41): 135-155.
[154] Black F. Studies of stock market volatility changes[M]. In Proceedings of American Statistical Association, Business and Economic Statistics Section, 1976, 177-181.
[155] Pagan A R, Schwert G W. Alternative models for conditional stock volatility[J]. Journal of Econometrics. 1990, 45: 267-290.
[156] Engle R F, Ng V K. Measuring and testing the impact of news on volatility[J]. Journal of Finance. 1993, 48: 1749-1778.
[157] Simkowitz M A, Beedles W L. Asymmetric stable distributed security returns[J]. Journal of the American Statistical Association. 1980, 75: 306-312.
[158] Kon S J. Models of stock returns: a comparison[J]. Journal of Finance. 1984, 39: 147-165.
[159] Singleton J C, Wingender J. Skewness persistence in common stock returns[J]. Journal of Financial and Quantitative Analysis. 1986, 21: 335-341.
[160] Theodossiou P. Financial data and the skewed generalized t distribution[J]. Management Science. 1998, 44: 1650-1661.
[161] Alles L A, Kling J L. Regularitis in the variation of skewness in asset returns[J]. Journal of Financial Research. 1994, 17: 427-438.
[162] Bekaert G, Erb C B, Harvey C R, et al. Distributional characteristics of emerging market returns and asset allocation[J]. Journal of Portfolio Management. 1998, 24: 102-116.
[163] Erb C V, Harvey C R, Viskanta T E. New perspectives on emerging market bonds[J]. Journal of Portfolio management. 1999, 25: 83-92.
[164] Li X, Xu Q. Evaluating density forecasts of the model with a conditional skewed-t distribution for China's stock markets[J]. Applied Financial Econometrics. 2008(18): 213-227.
[165] Liu H, Lee Y, Lee M. Forecasting China Stock Markets Volatility via GARCH Models under skewed GED distribution[J]. Journal of Money, Investment and Banking. 2009(7): 5-15.
[166] Harvey C R, Siddique A. Autoregressive Conditional Skewness[J]. The Journal of Financial and Quantitative Analysis. 1999, 34(4): 465-487.
[167] Hansen B E. Autoregressive Conditional Density Estimation[J]. International Economic Review. 1994, 35(3): 705-730.
[168] Harvey C R, Siddique A. Conditional skewness in asset pricing tests[J]. Journal of Finance. 2000, LV(3): 1263-1295.
[169] Wilhelmsson A. Value at Risk with time-varying variance, skewness and kurtosis - The NIG-ACD model[J]. The Econometrics Journal. 2009, 12: 82-104.
[170] Bollerslev T. A Conditional Heteroskedastic Time Series Model for Speculative Prices and Rates of Return[J]. Review of Economic Statistics. 1987, 69: 542-547.
[171] Nelson D. Conditional Heteroskedasticity in Asset Returns: A New Approach[J]. Econometrica. 1991, 59(2): 347-370.
[172] Fernandez C, Steel M. On Bayesian modelling of fat tails and skewness[J]. Journal of American Statistical Association. 1998, 93: 359-371.
[173] Barndorff-Nielsen O. Exponentially decreasing ditributions for the logarithm of particle size[C]. London: Royal Society London, 1977.
[174] Prause K. The generalized hyperbolic model: estimation, financial derivatives, and risk measures[D]. University of Freiburg, 1999.
[175] Protassov R. EM-based maximum likelihood parameter estimation for multivariate generalized hyperbolic distributions with fixed lambda[J]. Statistics and Computing. 2004, 14(1): 1-25.
[176] Protassov R. EM-based maximum likelihood parameter estimation for multivariate generalized hyperbolic distributions with fixed lammda[J]. Statistics and Computing. 2004, 14(1): 24-65.
[177] Forsberg L, Bollerslev T. Bridging the gap between the distribution of realized (ECU) volatility and GARCh modeling (of the EURO): the GARCH-NIG model[J]. Journal of Applied Econometrics. 2002(17): 535-548.
[178] Mcneil A J, Frey R, Embrechts P. Quantitative Risk Management: Concepts, Techniques and Tools[M]. New Jersey: Princeton University Press, 2005.
[179] Barndorff-Nielsen O E, Blasid P. Hyperbolic distributions and ramifications: contributions to theory and application[M]. In Statistical Distributions in Scientific Work, Taillie C, Patil G, Baldessari B, Dordrecht:Reidel, 1981: 4, 19-44.
[180] Bibby B, Sorensen M. Hyperbolic processes in finance[R]. University of Aarhus, Aarhus School of Bussiness, 2001.
[181] Chakravarti, Laha, Roy. Handbook of Methods of Applied Statistics[M]. John Wiley and Sons, 1967.
[182] Tavares A B, Curto J D, Tavares G N. Modeling Heavy tails and Asymmetry using ARCH-type Models with Stable Paretian Distributions[J]. Nonlinear Dynamics. 2008, 51: 231-243.
[183] Engle R F, Lilien D M, Robins R P. Estimating tiem varying risk premium in the term-structure: the ARCH-M model[J]. Econometrica. 1987, 55(2): 391-407.
[184] Hansen P, Lunde A. A forecast comparison of variance models: does anything beat a GARCH(1,1)?[J]. Journal of Applied Econometrics. 2005(20): 873-889.
[185] Poon S, Granger C. Forecasting variance in financial markets: a review[J]. Journal of Economic Literature. 2003(41): 478-540.
[186] Zivot E, Wang J. Modeling Financial Time Series with S-Plus[M]. Second Edition ed. Boston: Springer, 2005.
[187] Bollerslev T. A conditionally heteroskedastic time series model of security preces and rates of return data[J]. Review of Economics and Statistics. 1987, 59: 542-547.
[188] French K R, Schwert G W, Stambaugh R F. Expected stock returns and volatility[J]. Journal of Financial Economics. 1987, 19: 3-29.
[189] Schwert W. Stock Volatility and the Crash of S87[J]. Review of Financial Studies. 1990, 3(77-102).
[190] Nelson D B, Foster D B. Asymptotic filtering theory for univariate ARCH models[J]. Econometrica. 1994, 62: 1-41.
[191] Higgins M L, Bera A K. A Class of Nonlinear ARCH Models[J]. International Economic Review. 1992, 33: 137-158.
[192] Sentana E. Quadratic ARCH models: A potential reinterpretation of ARCH models as second-order Taylor approximation[R]. London: London School of Economics and Political Science. 1991.
[193] Zakoian J M. Threshold Heteroskedasticity Models[J]. Journal of Economic Dynamics and Control. 1994, 15: 931-955.
[194] Glosten L, Jagannathan R, Runkle D. On the Relation between Expected Value and the Volatility of the Nominal Excess Return on stocks[J]. Journal of Finance. 1993, 48: 1779-1801.
[195] Ding Z, Granger C W J, Engle R F. A Long Memory Property of Stock Market Returns and a New Model[J]. Journal of Empirical Finance. 1993, 1: 83-106.
[196] Barndorff-Nielsen O E. Normal Inverse Gaussian Distributions and Stochastic Volatility Modelling[J]. Scandinavian Journal of Statistics. 1997, 24(1): 1-13.
[197] Andersson J. On the Normal Inverse Gaussian Stochastic Volatility Model[J]. Journal of Business & Economic Statistics. 2001, 19(1): 44-54.
[198] Jensen M B, Lunde A. The NIG-S&ARCH model: a fat-tailed, stochastic, and autoregressive conditional heteroskedastic volatility model[J]. Econometrics Journal. 2001(4): 319-342.
[199] Jorion P. Value at Risk: the New Benchmark for Managing Financial Risk[M]. Third edition ed. Boston: The McGraw-Hill Companies, Inc, 2007.
[200] Artzner P, Delbaen R, Heath D. Thinking Coherently[J]. Risk. 1997, 10(11): 68-71.
[201] Philippe Artzner R D D H. Coherent Measures of Risk[J]. Mathematical Finance. 1999, 9(3): 203-228.
[202] Crouhy M, Galai D, Mark R. Prototype risk rating system[J]. Journal of Banking & Finance. 2001, 25: 41-95.
[203] Bluhm C, Overbeck L, Wagner C. An introduction to credit risk modeling[M]. London: Chapman & Hall/CRC, 2003.
[204] Saunders A, Allen L. Credit Risk Measurement: New Approaches to Value at Risk and Other Paradigms[M]. Second Edition ed. New York: John Wiley & Sons, 2002: 95-97.
[205] Altman E I, Kao D L. The Implications of Corporate Bond Ratings Drift[J]. Finanical Analysts Journal. 1992(5): 64-75.
[206] Nickell P, Perraudin W, Varotto S. Stability of Rating Transitions[J]. Journal of Banking and Finance. 2001, 24(1/2): 203-228.
[207] Altman E I, Kishore V M. Defaults and return on high yield bonds: analysis through 1997[R]. working paper, New York University Salomon Centre. 1998.
[208] Gupton M, Finger C C, Bhatia M. CreditMetrics-Technical Document[EB/OL]. New York: Morgan Guaranty Trust Company, 1997.
[209] Wilson T. Portfolio credit risk, part I[J]. Risk. 1997(9): 111-117.
[210] Wilson T. Portfolio credit risk, part II[J]. Risk. 1997(10): 56-61.
[211] Hu Y T, Kiesel G R, Perraudin W. The estimation of transition matrics for sovereign credit ratings. working paper. 2001.
[212] Credit Suisse Financial Products. CreditRisk+: A Credit Risk Management Framework[EB/OL]. Swiss: Credit Suisse Financial Products, 1997.
[213] Rice J A. Mathematical Statistics and Data Analysis[M]. second edition ed. Duxbury Press, 1995.
[214] Duffie D, Garleanu N. Risk and Valuation of Collateralized Debt Obligations[J]. Financial Analysts Journal. 2001(1): 41-62.
[215] Schonbucher P J. Factor Models: Portfolio Credit Risk When Defaults are Correlated [J]. The Journal of Risk Finance. 2001, 3(1): 45-56.
[216] Kealhofer S, Bohn J. Portfolio management of default risk[R]. KMV. http://www.kmv.com., 2001.
[217] Crosbie P, Bohn J. Modeling default risk[R]. KMV . http://www.kmv.com., 2002.
[218] Hu W. Calibration of Multivariate Generalized Hyperbolic Distributions using the EM Algorithm, with Applications in Risk Management, Portfolio Optimazation adn Portfolio Credit Risk[D]. Florida State University, 2005.
[219] Coval J D, Jurek J W, Stafford E. The Economics of Structured Finance[J]. Journal of Economic Perspectives. 2009, 23(1): 3-25.
[220] Bl?sid P, S?rensen M K. 'HYP'---A computer program for analyzing data by means of the hyperbolic distribution[R]. Department of Theoretical Statistics, University of Aarhus, Denmark, 1992.
[221] Prause K. The generalized hyperbolic model: estimation, financial derivatives, and riskmeasures[D]. University of Feiburg, 1999.
[222] Lillestol J. Risk analysis and the NIG distribution[J]. Journal of Risk. 2000(4): 41-55.
[223] Dempster A P, Laird N M, Rubin D. Maximum likelihood from incomplete data via the EM algorithm[J]. Journal of the Royal Statistical Society: Series B (Statistical Methodology). 1977, 39: 1-38.
[224] Liu C, Rubin D. ML estimation of the t distribution using EM and its extensions, ECM and ECME[J]. Statistica Sinica. 1995(5): 19-39.
[225] Abramowitz M, Stegum I A. Handbook of Mathematical Functions[M]. New York: Dover, 1974: 78-96.
[226]徐晓肆,任若恩.信用等级转移分析[J].生产力研究. 2004(9): 42-44.
[227]杨怀东,郭亚军,明东.信用转移矩阵预测模型的比较分析[J].东北大学学报(自然科学版). 2004, 25(5): 508-510.
[228]张玲,曾维火.基于Z值模型的我国上市公司信用评级研究[J].财经研究. 2004, 30(6): 5-13.
[229]张玲,赵峰.信用风险转移矩阵在可转债定价研究中的应用[J].金融教学与研究. 2005(3): 40-42.
[230] Parker G, Barber T, Benoit B. Financial stability: EU leaders take credit rating agencies to task[N]. Financial Times, 30 January, 2008.
[231] International Organization of Securities Commissions. Report on the subprime crisis[R]. New York: International Organization of Securities Commissions, 2008.
[232] Financial Services Authority. Financial Risk Outlook 2008[R]. Landon: Financial Services Authority, 2008.
[233] Committee of European Securities Regulators. The role of credit rating agencies in structured finance: Consultation paper[R]. Paris: Committee of European Securities Regulators, 2008.
[234] Chung J, Duyn V A. SEC to report on credit rating agencies[N]. Financial Times, 23 April, 2008.
[235] International Organization of Securities Commissions. Code of conduct fundamentals for credit rating agencies (revised)[R]. Madrid: International Organization of Securities Commissions, 2008.
[236] The Financial Stability Forum. Financial risk outlook 2008[R]. Landon: The Financial Stability Forum, 2008.
[237] Securities Industry and Financial Markets Association. Recommendations of the securities industry and financial markets association credit rating agencies task force[R]. New York: Securities Industry and Financial Markets Association, 2008.
[238] Fender I, Kiff J. CDO rating methodology: some thoughts on model risk and its implications[J]. Journal of Credit Risk. 2005(1): 37-58.
[239] Mork?tter S, Westerfeld S. Rating model arbitrage in CDO markets: an empirical analysis[J]. International Review of Financial Analysis, doi:10.1016/j.irfa.2009.01.002. 2009.
[240] Duff A, Einig S. Credit rating quality: the perceptions of market participants and other interested parties[J]. The British Accounting Review, doi:10.1016/j.bar.2008.11.003. 2009.
[241]王少波.资产证券化信用评级:国外模型方法及其借鉴[J].当代经济科学. 2007(6): 37-44.
[242]李扬.谈资产支持证券信用风险控制中的信用评级[J].财会月刊. 2008(2): 35-37.
[243]朱宸刚.信贷资产证券化-CDO产品信用评级方法对投资者保护的影响分析[J].金融与经济. 2008(2): 62-65.
[244]张明.透视CDO:类型、构造、评级与市场[J].国际金融研究. 2008(6): 40-48.
[245] Gluck J, Remeza H. Moody’s approach to rating multi-sector CDOs[R]. New York: Moody’s Investors Service Structured Finance Special Report, 2008.
[246] Witt G. Moody’s correlated binomial default distribution[R]. New York: Moody’s Investor Service Structured Finance Special Report, 2005.
[247] Peretyatkin V, Perraudin W. EL and DP approaches to rating CDO and the scope for‘ratings shopping’[M]. in Credit Ratings-methodologies, Rationale and Default Risk, Ong M K, London:Risk Books, 2002.
[248] National Economic Research Association. Credit ratings for structured products[R]. London: National Economic Research Association, 2003.
[249] Duffie D, Singleton J C. Credit Risk: Pricing, Measurement and Management[M]. New Jersey: Princeton University Press, 2003.
[250] Lando D. Credit Risk Modeling: Theory and Applications[M]. New Jersey: Princeton University Press, 2004.
[251] Ramaswamy S. Managing Credit Risk in Corporate Bond Portfolios[M]. New Jersey: John Wiley & Sons, Inc., 2004.
[252]王树同.抵押债务债券(CDO)风险收益特征及其在我国的发展前景[J].新金融. 2007(5): 46-50.