基于宏观经济因子的我国商业银行信用风险度量研究
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摘要
宏观经济波动给各国商业银行的稳健经营都带来了巨大冲击,宏观经济因子是银行信用风险度量必须考虑的重要参数。我国商业银行主要是采用定性分析或者简单量化的方法来估计宏观经济因子对信用风险的影响,未能进行较精确的模型化的度量。国外的信用风险度量模型也只考察经济周期因子而没有涵盖我国特殊的经济体制改革因子,不适合在我国直接应用。美国金融危机之后,我国银行监管部门加强了对商业银行的宏观审慎监管,更加注重对系统性风险的防范。在此背景下,研究基于宏观经济因子的商业银行信用风险度量具有重要的现实意义。本文首先从理论上分析宏观经济对我国商业银行信用风险的影响,然后分两步构建基于宏观经济因子的信用风险度量模型,进而将宏观经济分析和信用风险度量模型有机结合,形成基于宏观经济因子预测的信用风险度量方法。
已有研究还未能为经济周期以及经济体制改革对商业银行信用风险的影响提供完整的理论解释。本文通过构建一个简单的跨周期模型来分析经济周期对商业银行信用风险的影响机制,并刻画出银行预期损失和非预期损失的顺经济周期波动。经济体制改革方面,本文着重研究企业产权制度变革、金融体系改革以及对外开放对商业银行信用风险的影响：建立一个两部门风险生成模型来剖析企业产权制度变革对商业银行信用风险的影响机制；指出金融体系改革对商业银行存在双重硬化效应——预算约束硬化和资本约束硬化,进而影响商业银行信用风险；建立一个对外开放三阶段模型,指出对外开放在增加企业收入的同时也会加大企业收入的波动性,因而对银行信用风险产生双向的影响。我国经济体制改革尚未完成,因此经济体制改革因子是我国商业银行当前及未来信用风险度量都必须考虑的重要参数。
根据理论分析的结果,本文分两步来建立基于宏观经济因子的信用风险度量模型。第一步,通过建立宏观经济因子测定模型(Macroeconomic Factor Determine模型,简称MFD模型),从经济周期和经济体制改革两方面测定影响我国商业银行信用风险的宏观经济因子。实证分析确定了五个宏观经济因子：GDP增长率、通货膨胀率、企业产权多元化指数、金融市场化指数和外贸依存度,说明在我国商业银行信用风险度量中,经济体制改革因子和经济周期因子同样重要。第二步,运用宏观经济因子将测算借款企业违约概率的Logistic模型扩展为MF-Logistic模型(Macroeconomic Factor-Logistic Model),并通过实证分析得到分行业和分地区的MF-Logistic拟合模型。实证结果显示：各个MF-Logistic拟合模型均具有较好的拟合效果,各个宏观经济因子具有不同程度的显著性。MF-Logistic模型的信用风险判别能力高于传统的Logistic模型,并能量化宏观经济变化对企业违约概率的影响,为商业银行更科学的信用风险度量提供了基础模型。
在此基础上,本文以宏观经济因子预测为基础,计量银行贷款组合在未来不同宏观经济情景下的非预期损失。本文首先分析我国宏观经济的变化趋势,然后根据压力测试方法以及MFD模型预测宏观经济因子并组成未来三种不同的宏观经济情景,再运用MF-Logistic模型和基于频带划分的CreditRisk+模型计量贷款组合在不同的宏观经济情景下的非预期损失。基于宏观经济因子预测的信用风险度量方法能有效量化未来的宏观经济变化对商业银行信用风险的影响,有助于商业银行改变遵循“摩根规则”的信用风险度量模式,进而缓解经济资本的顺周期性,满足宏观审慎监管的要求。
我国商业银行应用基于宏观经济因子的信用风险度量方法,需要加强对宏观经济因子的监测,建立经济资本的顺周期缓释机制,以及运用MF-Logistic模型开展信用风险压力测试。
Macroeconomic environment is an important factor that must be considered in commercial banks' credit risk management. However, China's commercial banks mainly use qualitative or simple quantitative method to estimate the impact of macroeconomic factors in credit risk management and failed to allow for more accurate modeling measurement. Some relatively mature credit risk models that developed by foreigners take only economic cycle into account but without considering economic reform that happens in China, so are not suitable for direct application in China. After the U.S. financial crisis, China's banking supervision department strengthen the macro-prudential supervision to commercial banks and focus on systemic risks. Therefore, credit risk measurement based on macroeconomic factors is of important practical significance. The dissertation firstly analyzes the impact of macroeconomic to commercial banks credit risk. Then establishes credit risk measurement model based on macroeconomic factors. Finally organically combine the macroeconomic analysis and credit risk measurement model to take credit risk measurement based on macroeconomic factors.
Current economic theories fail to explain how the economic cycle and economic reform affect on commercial banks'credit risk. The dissertation builds up a simple Cross-Cycle model to analyze how the economic cycle's influence on commercial bank credit risk and describe the pro-cyclical varies of expected loss and non-expected loss of bank. Economic reform mainly affect the credit risk of commercial banks from three aspects of enterprise property system reform, financial system reform and opening up. The dissertation firstly establishs a Two-Sector Risk Generating model to analyze how the enterprise property system reform effects on the credit risk; secondly points out that the financial system reform has double harden effect to budget constraint and capital constraint, then influence the credit risk; thirdly establish an Opening-up 3 period model to illustrate that opening up to increases business revenue and also increase the volatility of revenue, which generate two-way effect on the banking credit risk. Chinese economic reform has not been completed. Therefore, the economic reform factors must be considered in China's commercial banks'current and future credit risk measurement.
According to the theoretic analysis, the dissertation establishes a credit risk measurement model based on macroeconomic factors. The first step builds up a Macroeconomic Factor Determine model (short for MFD model) and estimates the macoreconomic factors that significantly influece banking credit risk. Demonstration study determines five factors as GDPGR, INF, EPDI, FLI and FTD, indicating that economic reform factors are as important as economic cycle factors to credit risk measurement. The second step extends the Logistic model to Macroeconomic Factor-Logistic (MF-Logistic) model and takes empirical analysis of MF-Logistic model in industrial and regional level. Empirical analysis shows that the MF-Logistic models get good fitted result, all macroeconomic factors show varying significant degrees and have economic meanings. The MF-Logistic model enhances the judging ability to credit risk and can describ the impact of macroeconomic changes on probability of corporate defaults and provides model for scientific credit risk measurement.
Combining macro analysis, Press Test, MFD model, MF-Logistic model and CreditRisk+model, the dissertation measures the PD and economic capital of loans in different macroeconomic scenes based on macroeconomic factors forcast. credit risk measurement method based on macroeconomic factors prediction can directly reflect future macroeconomic factors affect on banking credit risk, can help banks to change credit risk measurement following "Morgan rule", then alleviate the procyclicality of economic capital and achieve unity of macro-prudent and micro-prudent.
China's commercial banks should strengthen inspection to macroeconomic factors, establish economic capital procyclicality buffer mechanism, implement refined credit risk measurement in industry and regional level, and prosecute credit risk press test.
已有研究还未能为经济周期以及经济体制改革对商业银行信用风险的影响提供完整的理论解释。本文通过构建一个简单的跨周期模型来分析经济周期对商业银行信用风险的影响机制,并刻画出银行预期损失和非预期损失的顺经济周期波动。经济体制改革方面,本文着重研究企业产权制度变革、金融体系改革以及对外开放对商业银行信用风险的影响：建立一个两部门风险生成模型来剖析企业产权制度变革对商业银行信用风险的影响机制；指出金融体系改革对商业银行存在双重硬化效应——预算约束硬化和资本约束硬化,进而影响商业银行信用风险；建立一个对外开放三阶段模型,指出对外开放在增加企业收入的同时也会加大企业收入的波动性,因而对银行信用风险产生双向的影响。我国经济体制改革尚未完成,因此经济体制改革因子是我国商业银行当前及未来信用风险度量都必须考虑的重要参数。
根据理论分析的结果,本文分两步来建立基于宏观经济因子的信用风险度量模型。第一步,通过建立宏观经济因子测定模型(Macroeconomic Factor Determine模型,简称MFD模型),从经济周期和经济体制改革两方面测定影响我国商业银行信用风险的宏观经济因子。实证分析确定了五个宏观经济因子：GDP增长率、通货膨胀率、企业产权多元化指数、金融市场化指数和外贸依存度,说明在我国商业银行信用风险度量中,经济体制改革因子和经济周期因子同样重要。第二步,运用宏观经济因子将测算借款企业违约概率的Logistic模型扩展为MF-Logistic模型(Macroeconomic Factor-Logistic Model),并通过实证分析得到分行业和分地区的MF-Logistic拟合模型。实证结果显示：各个MF-Logistic拟合模型均具有较好的拟合效果,各个宏观经济因子具有不同程度的显著性。MF-Logistic模型的信用风险判别能力高于传统的Logistic模型,并能量化宏观经济变化对企业违约概率的影响,为商业银行更科学的信用风险度量提供了基础模型。
在此基础上,本文以宏观经济因子预测为基础,计量银行贷款组合在未来不同宏观经济情景下的非预期损失。本文首先分析我国宏观经济的变化趋势,然后根据压力测试方法以及MFD模型预测宏观经济因子并组成未来三种不同的宏观经济情景,再运用MF-Logistic模型和基于频带划分的CreditRisk+模型计量贷款组合在不同的宏观经济情景下的非预期损失。基于宏观经济因子预测的信用风险度量方法能有效量化未来的宏观经济变化对商业银行信用风险的影响,有助于商业银行改变遵循“摩根规则”的信用风险度量模式,进而缓解经济资本的顺周期性,满足宏观审慎监管的要求。
我国商业银行应用基于宏观经济因子的信用风险度量方法,需要加强对宏观经济因子的监测,建立经济资本的顺周期缓释机制,以及运用MF-Logistic模型开展信用风险压力测试。
Macroeconomic environment is an important factor that must be considered in commercial banks' credit risk management. However, China's commercial banks mainly use qualitative or simple quantitative method to estimate the impact of macroeconomic factors in credit risk management and failed to allow for more accurate modeling measurement. Some relatively mature credit risk models that developed by foreigners take only economic cycle into account but without considering economic reform that happens in China, so are not suitable for direct application in China. After the U.S. financial crisis, China's banking supervision department strengthen the macro-prudential supervision to commercial banks and focus on systemic risks. Therefore, credit risk measurement based on macroeconomic factors is of important practical significance. The dissertation firstly analyzes the impact of macroeconomic to commercial banks credit risk. Then establishes credit risk measurement model based on macroeconomic factors. Finally organically combine the macroeconomic analysis and credit risk measurement model to take credit risk measurement based on macroeconomic factors.
Current economic theories fail to explain how the economic cycle and economic reform affect on commercial banks'credit risk. The dissertation builds up a simple Cross-Cycle model to analyze how the economic cycle's influence on commercial bank credit risk and describe the pro-cyclical varies of expected loss and non-expected loss of bank. Economic reform mainly affect the credit risk of commercial banks from three aspects of enterprise property system reform, financial system reform and opening up. The dissertation firstly establishs a Two-Sector Risk Generating model to analyze how the enterprise property system reform effects on the credit risk; secondly points out that the financial system reform has double harden effect to budget constraint and capital constraint, then influence the credit risk; thirdly establish an Opening-up 3 period model to illustrate that opening up to increases business revenue and also increase the volatility of revenue, which generate two-way effect on the banking credit risk. Chinese economic reform has not been completed. Therefore, the economic reform factors must be considered in China's commercial banks'current and future credit risk measurement.
According to the theoretic analysis, the dissertation establishes a credit risk measurement model based on macroeconomic factors. The first step builds up a Macroeconomic Factor Determine model (short for MFD model) and estimates the macoreconomic factors that significantly influece banking credit risk. Demonstration study determines five factors as GDPGR, INF, EPDI, FLI and FTD, indicating that economic reform factors are as important as economic cycle factors to credit risk measurement. The second step extends the Logistic model to Macroeconomic Factor-Logistic (MF-Logistic) model and takes empirical analysis of MF-Logistic model in industrial and regional level. Empirical analysis shows that the MF-Logistic models get good fitted result, all macroeconomic factors show varying significant degrees and have economic meanings. The MF-Logistic model enhances the judging ability to credit risk and can describ the impact of macroeconomic changes on probability of corporate defaults and provides model for scientific credit risk measurement.
Combining macro analysis, Press Test, MFD model, MF-Logistic model and CreditRisk+model, the dissertation measures the PD and economic capital of loans in different macroeconomic scenes based on macroeconomic factors forcast. credit risk measurement method based on macroeconomic factors prediction can directly reflect future macroeconomic factors affect on banking credit risk, can help banks to change credit risk measurement following "Morgan rule", then alleviate the procyclicality of economic capital and achieve unity of macro-prudent and micro-prudent.
China's commercial banks should strengthen inspection to macroeconomic factors, establish economic capital procyclicality buffer mechanism, implement refined credit risk measurement in industry and regional level, and prosecute credit risk press test.
引文
①数据来源：路透社网站《美国金融焦点》http://cn.reuters.com/
①数据来源：中国人民银行网站http://www.pbc.gov.cn/
[1]International Monetary Fund. Lessons of the Financial Crisis for Future Regulation of Financial Institutions and Market and for Liquidity Management[R]. workingpaper,2009:11-12
[2]Financial Services Authority (FSA). A Regulatory Response to the Global Banking Crisis[R]. workingpaper,2009,(2):40-41
[3]唐纳德·范·戴维特,今井贤志.信用风险模型与巴塞尔协议[M].北京：中国人民大学出版社,2005：10-46
[4]O. Vasicek. Probability of Loss on Loan Portfolio[R]. KMV Corporation, technical report,1987:56-73
[5]Michael B.Gordy. A Risk-Factor Model Foundation for Ratings-based Bank Capital Rules[J]. Journal of Financial Intermediation,2003,(12):199-232.
[6]Philipp J. Schonbucher. Factor Models for Portfolio Credit Risk[J]. Journal of Risk Finance,2001,3(1):45-56
[7]Susanne Emmer, Dirk Tashe. Calculating Credit Risk Capital Charges with the One-factor Model[J]. Journal of Risk,2004,7(5):85-103
[8]Paul H. Kupiec. Capital Allocation for Portfolio Credit Risk[J]. Journal of Financial Services Research,2007,32(1):103-122.
[9]Paul H. Kupiec. A Generalized Single Common Factor Model of Portfolio Credit Risk[J]. The Journal of Derivatives,2008,(1):25-40
[10]Paul H. Kupiec. How Well Does the Vasicek-Basel Airb Model Fit the Data? Evidence from a Long Time Series of Corporate Credit Rating Data[R]. FDIC Working Paper Series No.1523246.2009(11):98-103
[11]Michael B Gordy, D Jones. Capital allocation for securitizations with uncertainty in loss prioritization[R]. Washington DC:Federal Reserve Board Discussion Paper,2002:48-50
[12]L. Andersen, J. Sidenius & S. Basu. All your hedges in one basket[J]. Risk, 2003(11):6772.
[13]Gibson, M. Understanding the Risk in Synthetic CDOs[R]. Federal Reserve Board Finance and Economics Discussion Paper,2004,(4):36
[14]Wilson Thomas. Portfolio Credit Risk (1)[J]. Risk,1997,10(9):111-117
[15]Black F, Scholes M. The pricing of options and corporate liabilities[J]. Journal of Political Economy,1973,81(3):637-654
[16]Merton R C. On the pricing of corporate debt:The risk structure of interest rates[J]. Journal of Finance,1974,29(3):449-470
[17]Pesaran, M. Hashem, Til Schuermann, Bjorn-Jakob Treutler and Scott M. Weiner. Macroeconomic Dynamics and Credit Risk:A Global Perspective[J]. Journal of Credit, Money and Banking,2006,38(5):1211-1261
[18]Credit Suisse Financial Products. CreditRisk+:A Credit Risk Management Framework[R], Credit Suisse Financial Products,1997:11-78
[19]Michael B.Gordy. Saddlepoint approximation of CreditRisk+[J]. Journal of Banking & Finance,2002,26(7):1335-1353
[20]Giese G,. Enhancing CreditRisk+[J], Journal of Risk.2003,16(4):73-77
[21]Amogh Deshpande, Srikanth K. Iyer. The CreditRisk+Model With General Sector Correlations[J]. Central European Journal of Operations Research, 2009,17(2):219-228
[22]Vandendorpe A, Ngoc-Diep H, Vanduffel S et al. On the parameterization of the CreditRisk+model for estimating credit portfolio risk[J]. Insurance, Mathematics and Economics,2008,42 (2):736-745
[23]Frye J. Depressing recoveries [J]. Journal of Risk,2000,(11):108-111
[24]Andre Lucas, Pieter Klaassen, Peter Spreij, Stefan Straetmans. Tail behaviour of credit loss distributions for general latent factor models[J]. Applied Mathematical Finance,2003,10(4):337-357
[25]Carey, M., and Michael B. Gordy. Measuring Systematic Risk in Recoveries on Defaulted Debt I:Firm-Level Ultimate LGDs[R]. U.S. Federal Reserve Board, 2004:115-160
[26]Edward I. Altman, Brooks Brady, Andrea Resti, Andrea Sironi. The Link between Default and Recovery Rates:Theory, Empirical Evidence, and Implications[J]. Journal of Business,2005,78(6):2203-2228
[27]M. Crouhy, D. Galai, R. Mark. A comparative analysis of current credit risk models [J].Journal of Banking and Finance,2000,24(5):59-117.
[28]Pamela Nickell, William Perraudin, Simone Varotto. Stability of rating transitions [J]. Journal of Banking & Finance,2000,24(1):203-227
[29]Dimitrios Kavvathas. Estimating Credit Rating Transition Probabilities for Corporate Bonds[R]. AFA New Orleans Meetings working paper,2001:20-28
[30]Anil Bangia, Francis X. Diebold, Andre Kronimus, Christian Schagen, Til Schuermann. Ratings migration and the business cycle, with application to credit portfolio stress testing[J]. Journal of Banking & Finance,2002,26(3):445-474
[31]Siem Jan Koopman, Andre Lucas, Pieter Klaassen. Pro-Cyclicality, Empirical Credit Cycles, and Capital Buffer Formation[R]. Tinbergen Institute Discussion Papers No.02107,2002:36-50
[32]Saurina, Trucharte. An assessment of Basel Ⅱ procyclicality in mortgage portfolio[R]. Banco de Espana working paper 0712,2006:31-32
[33]Panetta, F. and Angelini,P., et al. Financial sector pro-cyclicality:lessons from crisis[J]. Bank of Italy Occasional Paper No.44,2009:79-92
[34]Financail Stability Forum (FSF). Report of the Financail Stability Forum on Addressing Procyclicality in the Financial System[R].working paper,2009: 26-39
[35]Jean-charles Rochet. Procyclicality of financial systems:is there a need to modify current accounting and regulatory rules?[J]. Financial Stability Review, 2008,(12):95-99
[36]Basel Committee on Banking Supervision (BCBS). Fair value measurement and modeling:An assessment of challenges and lesson learned from the market stress[R]. Basel Committee Report.137,2008:17-42
[37]Basel Committee on Banking Supervision. Supervisory guidance for assessing banks financial instrument fair value practice[R]. Basel Committee Report 153, 2009:32-40
[38]Brunnermeier, Crockett, Goodhart Persaud, Shin. The Fundamental Principles of Financial Regulation[R]. Switzerland International Center for Monetory and Banking Studies,2009(2):1-90
[39]Emilios Avgouleas. Financial Regulation, Behavioral Finance and the Global Financial Crisis:in Search of a New Regulatory Model[R]. School of Law, University of Manchester working paper,2009:265-354
[40]梁世栋,郭众,李勇.信用风险模型比较分析[J].中国管理科学,2002,(2)：15-20
[41]管七海,冯宗宪.违约概率测度研究：文献综述与比较[J].世界经济.2004,(11)：26-31
[42]曹道胜,何明升.商业银行信用风险模型的比较及其借鉴[J].金融研究,2006,(10)：68-75
[43]章政,田侃,吴宏.现代信用风险度量技术在我国的应用方向研究[J].金融研究,2006,(7)：11-18
[44]谢赤,徐国嘏.银行信用风险度量CreditMetricsTM模型与CPV模型比较研 究[J].湖南大学学报(自然科学版),2006,33(2)：135-137
[45]肖北溟.宏微观分析相结合的信贷风险预测模型研究[J].金融论坛,2004,(10)：57-61
[46]莫易娴,周好文.我国集群的地区维度信用风险实证分析[J].世界经济与政治论坛,2006(1)：40-45
[47]莫易娴,周好文.CPV模型研究产业集群的经济周期性风险[J].经济管理,2006(1)：32-37
[48]张宗益,胡纯.基于行业分类的商业银行信用风险的度量[J].统计与决策,2006,(6)：80-84
[49]靳凤菊.基于CPV模型的房地产信贷信用风险的度量和预测[J].金融论坛,2007,12(9)：40-43
[50]彭建刚,吕志华.基于行业特性的多元系统风险因子CreditRisk+模型[J].中国管理科学,2009,17(3)：56-64
[51]彭建刚,张丽寒,刘波,屠海波.聚合信用风险模型在我国商业银行应用的方法论探讨[J].金融研究,2008(08)：72-85
[52]张文锋.地区信用评级方法研究[J].上海金融,2007,(6)：45-47
[53]管七海.建立我国贷款企业违约率测度的多维度分析体系研究[J].金融论坛,2005,(8)：41-45
[54]贾海涛,邱长溶.宏观因素对贷款企业违约率影响的实证分析[J].现代管理科学,2009,(2)：67-72
[55]曼努·埃尔阿曼.杨玉明译.信用风险评估——方法·模型·应用[M].北京：清华大学出版社,2004：1-2
[56]彭建刚.商业银行管理学(第二版)[M].北京：中国金融出版社.2009：76-272
[57]詹原瑞.银行信用风险的现代度量与管理[M].北京：经济科学出版社,2004：5-87
[58]Basel Committee on Banking Supervision. International Convergence of Capital Measurement and Capital Standards:A Framework, Comprehensive Version[R]. working paper,2006:313-356
[59]李志辉,现代信用风险量化度量和管理研究[M].北京：中国金融出版社,2001：73-89
[60]迪迪埃·科森,于格·皮罗特.高级信用风险分析[M].北京：机械工业出版社,2005：112-116
[61]菲利普·乔瑞.风险价值VAR[M].北京：中信出版社,2005：5-128
[62]Basel Committee on Banking Supervision. Third consultative paper[R].2003: 346-405
[63]米歇尔·科罗赫,丹·加莱,罗伯特·马克.风险管理[M].北京：中国财政经济出版社,2005：231-252
[64]巴曙松.巴塞尔新资本协议研究[M].北京：中国金融出版社,2003：235-370
[65]Greg M. Gupton & Roger M.Stein. Loss Calc:Moody's Model for Predicting Loss Given Default [R]. Moody's Investor Service,2001:2-10
[66]迈克尔·王.内部信用风险模型——资本分配和绩效度量[M].天津：南开大学出版社,2004：6-125
[67]武剑.商业银行经济资本配置与管理——全面风险管理之核心工具[M].北京：中国金融出版社,2009：95
[68]阿诺·德·瑟维吉尼,奥里维尔·雷劳特.信用风险——度量与管理[M].北京：中国财政经济出版社,2005：89-95
[69]Hyman Minsky. The Financial Fragility Hypothesis:Capitalist Process and the Behavior of the Economy [A]. CharlesP.Kindlberger and Jean-Pierre Laffargue. Financial Crises [C]. Cambridge:Cambridge University Press,1982.112-130
[70]Hyman Minsky. Stabilizing an Unstable Economy[M]. New Haven:Yale University Press,1986:65-68
[71]Kregel J. A. Margins of Safety and Weight of the Argument in Generating Financial Fragility [J]. Journal of Economics Issues,1997,31(2):650-668
[72]吴敬琏.当代中国经济改革[M].上海：上海远东出版社,2004：373-375
[73]林毅夫,李志赞.政策性负担、道德风险与预算软约束[J].经济研究,2004,(2)：17-26
[74]施华强,彭兴韵.商业银行软预算约束与中国银行业改革[J].金融研究,2003,(10)：1-16
[75]施华强.中国国有商业银行不良贷款内生性：一个基于双重软预算约束的分析框架[J].金融研究,2004,(6)：1-12
[76]王一江,田国强.不良资产处理、股份制改造与外资战略——中日韩银行业经验比较[J].经济研究,2004,(11)：28-36
[77]江曙霞、罗杰、黄君慈.信贷集中与扩张、软预算约束竞争和银行系统性风险[J].金融研究,2006,(4)：40-48
[78]高鹤.中国银行业不良资产生成机制：基于经济转型的分析框架[J].经济科学,2006,(12)：1-9
[79]赵先信.银行内部模型和监管模型[M].上海：上海人民出版社,2004：326-330
[80]Til Schuermann, Yusuf Jafry. Measurement and Estimation of Credit Migration Matrices[R]. Wharton School Center for Financial Institutions, University of Pennsylvania, Center for Financial Institutions Working Papers,2003:3-8
[81]Allen, Aaunders. A survey of cyclical effects in credit risk measurement model[R]. BIS Wording Paper No.126,2003:75-83
[82]Pierre Collin-Dufresne, Robert S. Goldstein Do Credit Spreads Reflect Stationary Leverage Ratios?[J]. Journal of Finance,2001,56(5):1929-1958
[83]Schroeck G. Risk Management and Value Creation in Financial Institutions[M]. New York:John Wiley and Sons Press,2002:15-202
[84]Zaik E, Walter J,Kelling J G. RAROC at Bank of America:From Theory to Practice[J]. Journal of Applied Finance,1996,(9):83-93
[85]Vasicek O. Loan portfolio value[J]. Risk,2002,(12):160-162
[86]Jacobson T, Linde J, Roszbach K. Credit Risk versus Capital Requirements under Basel Ⅱ:Are SME loans and retail credit really different? [J]. Journal of Financial Services Research,2005,28(3):43-75
[87]Laeven R J A, Goovaerts M J, Hoedemakers T. Some asymptotic results for sums of dependent random variables, with actuarial applications [J]. Insurance, Mathematics and Economics,2005, (37):154-172
[88]Milne A, Onorato M. Apples and Pears. the Comparison of Risk Capital and Required Return in Financial Institutions[R]. Working paper,2007:215-220
[89]Kass R,Goovaerts M J,Dhaene J,et al. Modern Actuarial Risk Theory[M]. Lundon:Kluwer Academic Publishers,2001:1-420
[90]Laeven R J A, Goovaerts M J. An Optimization Approach to the Dynamic Allocation of Economic Capital[J]. Insurance Mathematics and Economics,2004, 35:299-319
[91]Goovaerts M J, Borre E V D, Laeven R J A. Managing Economic and Virtual Economic Capital within Financial Conglomerates. http:www.ssrn.com, 2005-02-07
[92]Gourieroux C, Laurent J P, Scaillet O. Sensitivity analysis of Values at Risk. Journal of Empirical Finance,2000, (7):225-245
[93]Gordy M B. A comparative anatomy of Credit Risk models. Journal of Banking & Finance,2000, (24):119-149
[94]Denault M. Coherent Allocation of Risk Capital. Journal of Risk,2001, (4):7-21
[95]Haaf H, Reiss O, Schoenmakers B. Numerically stable computation of CreditRisk+. Technical report,2003,8-15
[96]Kalkbrener M. An Axiomatic Approach to Capital Allocation. Mathematical Finance,2005,15:425-437
[97]Hierry Ane, Cecile Kharoubi. Dependence structure and risk measure[J]. The Journal of Business,2003,76(3):411-438
[98]McNeil A, Frey R, Embrechts P. Quantitative Risk Management. Princeton University Press,2005,540-598
[99]Tasche D. Capital Allocation for Credit Portfolios with Kernel Estimators. Working Paper, Fitch Ratings,2007
[100]Tasche D. Euler Allocation:Theory and Practice, www.ssrn.com,2007-8-19
[101]Stoughton N M, Zechner J. Optimal Capital Allocation Using RAROC and EVA. Jounal of Financial Intermediation,2007,16:312-342
[102]Buch A, Dorfleitner G. Coherent Risk Measures, Coherent Capital Allocations and the Gradient Allocation Principle[J]. Insurance:Mathematics and Economics, 2008,42:235-242
[103]Bens S. Bernanke, Cara S. Lown, and Benjamin M. Friedman. The Credit Crunch[R]. Brookings Papers on Economic Activity 1991, (2):205-247
[104]Gordy M, Howells B. Procyclicality in Basel Ⅱ:Can we treat the disease without killing the patient?[J]. Journal of Financial Intermediation,2006,15(3):395-417
[105]Alan Greenspan. the Age of Turbulence:Adventures in a New World[M]. United Kingdom:The Penguin Press HC,2007,(9):1-544
[106]J. Ayuso, D.Perez, J. Saurina. Are capital buffers pro-cyclical? Evidence from Spanish panel data[J]. Journal of Financial Intermediation,2004,13(2):249-264
[107]周小川.关于改变宏观和微观顺周期性的进一步探讨[J].中国金融,2009,(8)：8-11.
[108]Kornai, Janos. Economics of Shortage[M]. Amsterdam:North-Holland.1980: 23-53
[109]Kornai, Janos, Maskin, Eric and Gerard Roland. Understanding the Soft Budget Constraint[R]. Working Paper, UC Berkeley,2002:35-39
[110]秦晖.中国转型之路的前景[J].战略与管理,2003,(1)：1-20
[111]深圳证券交易所综合研究所.股权分置改革的回顾与总结[R].研究报告第0147号,2006.12.20：19-38
[112]张杰.制度、渐进转轨与中国金融改革[M].北京：中国金融出版社,2001：209-210
[113]易宪容.中国利率市场化的未来发展[J].中国经济时报,2004-12-02
[114]Joseph Stiglitz. Discount Rates:The Rate of Discount for Benefit-Cost Analysis and the Theory of the Second Best[A], In Richard Layard and Stephen Glaister, eds. Cost-Benefit Analysis,2nd ed. London:Cambridge University Press,1994: 415-459
[115]Dewatripont, Mathias & Eric Maskin. Credit and Efficiency in Centralism and Decentralized Economics[J]. Review of Economics Studies,1995,62(4):541-556
[116]Nicolaas Groenewold, Jiangang Peng, Guanzheng Li, Xiangmei Fan. Financial liberalisation or financial development? Tests using a Delphi-based index of liberalisation for China [J]. International Journal of Banking and Finance, 2008,(1):95-115
[117]吴世农,卢贤义.我国上市公司财务困境预测模型研究[J].经济研究,2001,(6)：46-55
[118]赵健梅,王春莉.财务危机预警在我国上市公司的实证研究[J].数量经济技术经济研究,2003,(7)
[119]李萌.Logit模型在商业银行信用风险评估中的应用研究[J].管理科学,2005,(4)：33-38
[120]石晓军.Logistic违约率模型最优样本配比与分界点的模拟分析[J].数理统计与管理,2006,(6)：675-682
[121]鲜文铎,向锐.基于混合Logit模型的财务困境预测研究[J].数量经济技术经济研究,2007,(9)：79-85
[122]彭建刚,易宇,李樟飞.商业银行贷款违约概率的测算方法探讨：贷款违约表法[J].管理学报,2008,(6)：12-18
[123]Krishan Nagpal, Reza Bahar. Measuring default correlation, Risk,2001,(3): 129-132
[124]华晓龙.基于宏观压力测试方法的商业银行体系信用风险评估[J].数量经济技术经济研究,2009,(4)：117-128
[125]韩岗.国外信用风险度量方法及其适用性研究[J].国际金融研究,2008,(3)：43-48.
[126]Martin D. Early warning of bank failure:a logit regression approach[J]. Journal of Banking and Finance,1977(3):249-276
[127]West David. Neural network credit scoring models [J]. Computer & Operations Research,2000,27(1):1131-1152
[128]彭建刚,屠海波,何婧,周颖辉.有序多分类logistic模型在违约概率测算的应用[J].财经理论与实践,2009,(4)：2-7
[129]卫海英.SPSS 10.0 for Windows在经济管理中的应用[M].北京：中国统计出版社,2003：248-271
[130]万柏坤,薛召,李佳等.应用roc曲线优选模式分类算法[J].自然科学进展,2006,(11)：1511-1516
[131]J.Williamson. Regional inequality and the proces of national development [J]. Economic Development and Culture Change,1965,(4):203-221
[132]于立勇,詹捷辉.基于Logistic回归分析的违约概率预测研究[J].财经研究,2004,(9)：69-75
[133]魏杰.30年中国对外开放战略的变革[J].理论前沿,2008,(10)：9-13
[134]钟伟,宛圆渊.预算软约束和金融危机理论的微观建构[J].经济研究,2001,(8)：44-52
[135]李文泓.关于宏观审慎监管框架下逆周期政策的探讨[J].金融研究,2009,59(7)：7-24
[136]王胜邦,陈颖.新资本协议内部评级法对宏观经济运行的影响：亲经济周期效应研究[J].金融研究,2008,(5)：48-64
[137]Repullo, R., Saurina, J. and Trucharte, C. Mitigating the procyclicality of Basel Ⅱ [A]. in Dewatripont, M., Freixas, X., and Portes, R., Macroeconomics Stability and Financial Regulation:Key issues for the G20[R], Center for Economic Policy Research, London, UK,2009:11-23
[138]Castren, Olli, Fitzpatrick, Trevor, Matthias Sydow. Assessing Portfolio Credit Risk Changes in a Sample of EU Large and Complex Banking Groups in Reaction to Macroeconomic Shocks[R]. ECB Working Paper No.1002.2009,(2): 78-86
[139]Piergiorgio Alessandri, Prasanna Gai, Sujit Kapadia, Nada Mora & Claus Puhr. Towards a framwork for quantifying systemic stability [J]. International Journal of Central Banking,2009,5 (9):47-81
[140]Fiori, Foglia and Iannotti. Beyond Macroeconomic risk:the role of contagion in the Italian corporate default correlation [C]. San Paolo:Annual Seminar on Banking, Financial stability and Risk,2008,(5):120-138
[141]Bank of Japan. The Framework for Macro Stress-Testing of Credit Risk: Incorporating Transition in Borrower Classifications[R], Financial System Report,2007(9):17-36
①数据来源：中国人民银行网站http://www.pbc.gov.cn/
[1]International Monetary Fund. Lessons of the Financial Crisis for Future Regulation of Financial Institutions and Market and for Liquidity Management[R]. workingpaper,2009:11-12
[2]Financial Services Authority (FSA). A Regulatory Response to the Global Banking Crisis[R]. workingpaper,2009,(2):40-41
[3]唐纳德·范·戴维特,今井贤志.信用风险模型与巴塞尔协议[M].北京：中国人民大学出版社,2005：10-46
[4]O. Vasicek. Probability of Loss on Loan Portfolio[R]. KMV Corporation, technical report,1987:56-73
[5]Michael B.Gordy. A Risk-Factor Model Foundation for Ratings-based Bank Capital Rules[J]. Journal of Financial Intermediation,2003,(12):199-232.
[6]Philipp J. Schonbucher. Factor Models for Portfolio Credit Risk[J]. Journal of Risk Finance,2001,3(1):45-56
[7]Susanne Emmer, Dirk Tashe. Calculating Credit Risk Capital Charges with the One-factor Model[J]. Journal of Risk,2004,7(5):85-103
[8]Paul H. Kupiec. Capital Allocation for Portfolio Credit Risk[J]. Journal of Financial Services Research,2007,32(1):103-122.
[9]Paul H. Kupiec. A Generalized Single Common Factor Model of Portfolio Credit Risk[J]. The Journal of Derivatives,2008,(1):25-40
[10]Paul H. Kupiec. How Well Does the Vasicek-Basel Airb Model Fit the Data? Evidence from a Long Time Series of Corporate Credit Rating Data[R]. FDIC Working Paper Series No.1523246.2009(11):98-103
[11]Michael B Gordy, D Jones. Capital allocation for securitizations with uncertainty in loss prioritization[R]. Washington DC:Federal Reserve Board Discussion Paper,2002:48-50
[12]L. Andersen, J. Sidenius & S. Basu. All your hedges in one basket[J]. Risk, 2003(11):6772.
[13]Gibson, M. Understanding the Risk in Synthetic CDOs[R]. Federal Reserve Board Finance and Economics Discussion Paper,2004,(4):36
[14]Wilson Thomas. Portfolio Credit Risk (1)[J]. Risk,1997,10(9):111-117
[15]Black F, Scholes M. The pricing of options and corporate liabilities[J]. Journal of Political Economy,1973,81(3):637-654
[16]Merton R C. On the pricing of corporate debt:The risk structure of interest rates[J]. Journal of Finance,1974,29(3):449-470
[17]Pesaran, M. Hashem, Til Schuermann, Bjorn-Jakob Treutler and Scott M. Weiner. Macroeconomic Dynamics and Credit Risk:A Global Perspective[J]. Journal of Credit, Money and Banking,2006,38(5):1211-1261
[18]Credit Suisse Financial Products. CreditRisk+:A Credit Risk Management Framework[R], Credit Suisse Financial Products,1997:11-78
[19]Michael B.Gordy. Saddlepoint approximation of CreditRisk+[J]. Journal of Banking & Finance,2002,26(7):1335-1353
[20]Giese G,. Enhancing CreditRisk+[J], Journal of Risk.2003,16(4):73-77
[21]Amogh Deshpande, Srikanth K. Iyer. The CreditRisk+Model With General Sector Correlations[J]. Central European Journal of Operations Research, 2009,17(2):219-228
[22]Vandendorpe A, Ngoc-Diep H, Vanduffel S et al. On the parameterization of the CreditRisk+model for estimating credit portfolio risk[J]. Insurance, Mathematics and Economics,2008,42 (2):736-745
[23]Frye J. Depressing recoveries [J]. Journal of Risk,2000,(11):108-111
[24]Andre Lucas, Pieter Klaassen, Peter Spreij, Stefan Straetmans. Tail behaviour of credit loss distributions for general latent factor models[J]. Applied Mathematical Finance,2003,10(4):337-357
[25]Carey, M., and Michael B. Gordy. Measuring Systematic Risk in Recoveries on Defaulted Debt I:Firm-Level Ultimate LGDs[R]. U.S. Federal Reserve Board, 2004:115-160
[26]Edward I. Altman, Brooks Brady, Andrea Resti, Andrea Sironi. The Link between Default and Recovery Rates:Theory, Empirical Evidence, and Implications[J]. Journal of Business,2005,78(6):2203-2228
[27]M. Crouhy, D. Galai, R. Mark. A comparative analysis of current credit risk models [J].Journal of Banking and Finance,2000,24(5):59-117.
[28]Pamela Nickell, William Perraudin, Simone Varotto. Stability of rating transitions [J]. Journal of Banking & Finance,2000,24(1):203-227
[29]Dimitrios Kavvathas. Estimating Credit Rating Transition Probabilities for Corporate Bonds[R]. AFA New Orleans Meetings working paper,2001:20-28
[30]Anil Bangia, Francis X. Diebold, Andre Kronimus, Christian Schagen, Til Schuermann. Ratings migration and the business cycle, with application to credit portfolio stress testing[J]. Journal of Banking & Finance,2002,26(3):445-474
[31]Siem Jan Koopman, Andre Lucas, Pieter Klaassen. Pro-Cyclicality, Empirical Credit Cycles, and Capital Buffer Formation[R]. Tinbergen Institute Discussion Papers No.02107,2002:36-50
[32]Saurina, Trucharte. An assessment of Basel Ⅱ procyclicality in mortgage portfolio[R]. Banco de Espana working paper 0712,2006:31-32
[33]Panetta, F. and Angelini,P., et al. Financial sector pro-cyclicality:lessons from crisis[J]. Bank of Italy Occasional Paper No.44,2009:79-92
[34]Financail Stability Forum (FSF). Report of the Financail Stability Forum on Addressing Procyclicality in the Financial System[R].working paper,2009: 26-39
[35]Jean-charles Rochet. Procyclicality of financial systems:is there a need to modify current accounting and regulatory rules?[J]. Financial Stability Review, 2008,(12):95-99
[36]Basel Committee on Banking Supervision (BCBS). Fair value measurement and modeling:An assessment of challenges and lesson learned from the market stress[R]. Basel Committee Report.137,2008:17-42
[37]Basel Committee on Banking Supervision. Supervisory guidance for assessing banks financial instrument fair value practice[R]. Basel Committee Report 153, 2009:32-40
[38]Brunnermeier, Crockett, Goodhart Persaud, Shin. The Fundamental Principles of Financial Regulation[R]. Switzerland International Center for Monetory and Banking Studies,2009(2):1-90
[39]Emilios Avgouleas. Financial Regulation, Behavioral Finance and the Global Financial Crisis:in Search of a New Regulatory Model[R]. School of Law, University of Manchester working paper,2009:265-354
[40]梁世栋,郭众,李勇.信用风险模型比较分析[J].中国管理科学,2002,(2)：15-20
[41]管七海,冯宗宪.违约概率测度研究：文献综述与比较[J].世界经济.2004,(11)：26-31
[42]曹道胜,何明升.商业银行信用风险模型的比较及其借鉴[J].金融研究,2006,(10)：68-75
[43]章政,田侃,吴宏.现代信用风险度量技术在我国的应用方向研究[J].金融研究,2006,(7)：11-18
[44]谢赤,徐国嘏.银行信用风险度量CreditMetricsTM模型与CPV模型比较研 究[J].湖南大学学报(自然科学版),2006,33(2)：135-137
[45]肖北溟.宏微观分析相结合的信贷风险预测模型研究[J].金融论坛,2004,(10)：57-61
[46]莫易娴,周好文.我国集群的地区维度信用风险实证分析[J].世界经济与政治论坛,2006(1)：40-45
[47]莫易娴,周好文.CPV模型研究产业集群的经济周期性风险[J].经济管理,2006(1)：32-37
[48]张宗益,胡纯.基于行业分类的商业银行信用风险的度量[J].统计与决策,2006,(6)：80-84
[49]靳凤菊.基于CPV模型的房地产信贷信用风险的度量和预测[J].金融论坛,2007,12(9)：40-43
[50]彭建刚,吕志华.基于行业特性的多元系统风险因子CreditRisk+模型[J].中国管理科学,2009,17(3)：56-64
[51]彭建刚,张丽寒,刘波,屠海波.聚合信用风险模型在我国商业银行应用的方法论探讨[J].金融研究,2008(08)：72-85
[52]张文锋.地区信用评级方法研究[J].上海金融,2007,(6)：45-47
[53]管七海.建立我国贷款企业违约率测度的多维度分析体系研究[J].金融论坛,2005,(8)：41-45
[54]贾海涛,邱长溶.宏观因素对贷款企业违约率影响的实证分析[J].现代管理科学,2009,(2)：67-72
[55]曼努·埃尔阿曼.杨玉明译.信用风险评估——方法·模型·应用[M].北京：清华大学出版社,2004：1-2
[56]彭建刚.商业银行管理学(第二版)[M].北京：中国金融出版社.2009：76-272
[57]詹原瑞.银行信用风险的现代度量与管理[M].北京：经济科学出版社,2004：5-87
[58]Basel Committee on Banking Supervision. International Convergence of Capital Measurement and Capital Standards:A Framework, Comprehensive Version[R]. working paper,2006:313-356
[59]李志辉,现代信用风险量化度量和管理研究[M].北京：中国金融出版社,2001：73-89
[60]迪迪埃·科森,于格·皮罗特.高级信用风险分析[M].北京：机械工业出版社,2005：112-116
[61]菲利普·乔瑞.风险价值VAR[M].北京：中信出版社,2005：5-128
[62]Basel Committee on Banking Supervision. Third consultative paper[R].2003: 346-405
[63]米歇尔·科罗赫,丹·加莱,罗伯特·马克.风险管理[M].北京：中国财政经济出版社,2005：231-252
[64]巴曙松.巴塞尔新资本协议研究[M].北京：中国金融出版社,2003：235-370
[65]Greg M. Gupton & Roger M.Stein. Loss Calc:Moody's Model for Predicting Loss Given Default [R]. Moody's Investor Service,2001:2-10
[66]迈克尔·王.内部信用风险模型——资本分配和绩效度量[M].天津：南开大学出版社,2004：6-125
[67]武剑.商业银行经济资本配置与管理——全面风险管理之核心工具[M].北京：中国金融出版社,2009：95
[68]阿诺·德·瑟维吉尼,奥里维尔·雷劳特.信用风险——度量与管理[M].北京：中国财政经济出版社,2005：89-95
[69]Hyman Minsky. The Financial Fragility Hypothesis:Capitalist Process and the Behavior of the Economy [A]. CharlesP.Kindlberger and Jean-Pierre Laffargue. Financial Crises [C]. Cambridge:Cambridge University Press,1982.112-130
[70]Hyman Minsky. Stabilizing an Unstable Economy[M]. New Haven:Yale University Press,1986:65-68
[71]Kregel J. A. Margins of Safety and Weight of the Argument in Generating Financial Fragility [J]. Journal of Economics Issues,1997,31(2):650-668
[72]吴敬琏.当代中国经济改革[M].上海：上海远东出版社,2004：373-375
[73]林毅夫,李志赞.政策性负担、道德风险与预算软约束[J].经济研究,2004,(2)：17-26
[74]施华强,彭兴韵.商业银行软预算约束与中国银行业改革[J].金融研究,2003,(10)：1-16
[75]施华强.中国国有商业银行不良贷款内生性：一个基于双重软预算约束的分析框架[J].金融研究,2004,(6)：1-12
[76]王一江,田国强.不良资产处理、股份制改造与外资战略——中日韩银行业经验比较[J].经济研究,2004,(11)：28-36
[77]江曙霞、罗杰、黄君慈.信贷集中与扩张、软预算约束竞争和银行系统性风险[J].金融研究,2006,(4)：40-48
[78]高鹤.中国银行业不良资产生成机制：基于经济转型的分析框架[J].经济科学,2006,(12)：1-9
[79]赵先信.银行内部模型和监管模型[M].上海：上海人民出版社,2004：326-330
[80]Til Schuermann, Yusuf Jafry. Measurement and Estimation of Credit Migration Matrices[R]. Wharton School Center for Financial Institutions, University of Pennsylvania, Center for Financial Institutions Working Papers,2003:3-8
[81]Allen, Aaunders. A survey of cyclical effects in credit risk measurement model[R]. BIS Wording Paper No.126,2003:75-83
[82]Pierre Collin-Dufresne, Robert S. Goldstein Do Credit Spreads Reflect Stationary Leverage Ratios?[J]. Journal of Finance,2001,56(5):1929-1958
[83]Schroeck G. Risk Management and Value Creation in Financial Institutions[M]. New York:John Wiley and Sons Press,2002:15-202
[84]Zaik E, Walter J,Kelling J G. RAROC at Bank of America:From Theory to Practice[J]. Journal of Applied Finance,1996,(9):83-93
[85]Vasicek O. Loan portfolio value[J]. Risk,2002,(12):160-162
[86]Jacobson T, Linde J, Roszbach K. Credit Risk versus Capital Requirements under Basel Ⅱ:Are SME loans and retail credit really different? [J]. Journal of Financial Services Research,2005,28(3):43-75
[87]Laeven R J A, Goovaerts M J, Hoedemakers T. Some asymptotic results for sums of dependent random variables, with actuarial applications [J]. Insurance, Mathematics and Economics,2005, (37):154-172
[88]Milne A, Onorato M. Apples and Pears. the Comparison of Risk Capital and Required Return in Financial Institutions[R]. Working paper,2007:215-220
[89]Kass R,Goovaerts M J,Dhaene J,et al. Modern Actuarial Risk Theory[M]. Lundon:Kluwer Academic Publishers,2001:1-420
[90]Laeven R J A, Goovaerts M J. An Optimization Approach to the Dynamic Allocation of Economic Capital[J]. Insurance Mathematics and Economics,2004, 35:299-319
[91]Goovaerts M J, Borre E V D, Laeven R J A. Managing Economic and Virtual Economic Capital within Financial Conglomerates. http:www.ssrn.com, 2005-02-07
[92]Gourieroux C, Laurent J P, Scaillet O. Sensitivity analysis of Values at Risk. Journal of Empirical Finance,2000, (7):225-245
[93]Gordy M B. A comparative anatomy of Credit Risk models. Journal of Banking & Finance,2000, (24):119-149
[94]Denault M. Coherent Allocation of Risk Capital. Journal of Risk,2001, (4):7-21
[95]Haaf H, Reiss O, Schoenmakers B. Numerically stable computation of CreditRisk+. Technical report,2003,8-15
[96]Kalkbrener M. An Axiomatic Approach to Capital Allocation. Mathematical Finance,2005,15:425-437
[97]Hierry Ane, Cecile Kharoubi. Dependence structure and risk measure[J]. The Journal of Business,2003,76(3):411-438
[98]McNeil A, Frey R, Embrechts P. Quantitative Risk Management. Princeton University Press,2005,540-598
[99]Tasche D. Capital Allocation for Credit Portfolios with Kernel Estimators. Working Paper, Fitch Ratings,2007
[100]Tasche D. Euler Allocation:Theory and Practice, www.ssrn.com,2007-8-19
[101]Stoughton N M, Zechner J. Optimal Capital Allocation Using RAROC and EVA. Jounal of Financial Intermediation,2007,16:312-342
[102]Buch A, Dorfleitner G. Coherent Risk Measures, Coherent Capital Allocations and the Gradient Allocation Principle[J]. Insurance:Mathematics and Economics, 2008,42:235-242
[103]Bens S. Bernanke, Cara S. Lown, and Benjamin M. Friedman. The Credit Crunch[R]. Brookings Papers on Economic Activity 1991, (2):205-247
[104]Gordy M, Howells B. Procyclicality in Basel Ⅱ:Can we treat the disease without killing the patient?[J]. Journal of Financial Intermediation,2006,15(3):395-417
[105]Alan Greenspan. the Age of Turbulence:Adventures in a New World[M]. United Kingdom:The Penguin Press HC,2007,(9):1-544
[106]J. Ayuso, D.Perez, J. Saurina. Are capital buffers pro-cyclical? Evidence from Spanish panel data[J]. Journal of Financial Intermediation,2004,13(2):249-264
[107]周小川.关于改变宏观和微观顺周期性的进一步探讨[J].中国金融,2009,(8)：8-11.
[108]Kornai, Janos. Economics of Shortage[M]. Amsterdam:North-Holland.1980: 23-53
[109]Kornai, Janos, Maskin, Eric and Gerard Roland. Understanding the Soft Budget Constraint[R]. Working Paper, UC Berkeley,2002:35-39
[110]秦晖.中国转型之路的前景[J].战略与管理,2003,(1)：1-20
[111]深圳证券交易所综合研究所.股权分置改革的回顾与总结[R].研究报告第0147号,2006.12.20：19-38
[112]张杰.制度、渐进转轨与中国金融改革[M].北京：中国金融出版社,2001：209-210
[113]易宪容.中国利率市场化的未来发展[J].中国经济时报,2004-12-02
[114]Joseph Stiglitz. Discount Rates:The Rate of Discount for Benefit-Cost Analysis and the Theory of the Second Best[A], In Richard Layard and Stephen Glaister, eds. Cost-Benefit Analysis,2nd ed. London:Cambridge University Press,1994: 415-459
[115]Dewatripont, Mathias & Eric Maskin. Credit and Efficiency in Centralism and Decentralized Economics[J]. Review of Economics Studies,1995,62(4):541-556
[116]Nicolaas Groenewold, Jiangang Peng, Guanzheng Li, Xiangmei Fan. Financial liberalisation or financial development? Tests using a Delphi-based index of liberalisation for China [J]. International Journal of Banking and Finance, 2008,(1):95-115
[117]吴世农,卢贤义.我国上市公司财务困境预测模型研究[J].经济研究,2001,(6)：46-55
[118]赵健梅,王春莉.财务危机预警在我国上市公司的实证研究[J].数量经济技术经济研究,2003,(7)
[119]李萌.Logit模型在商业银行信用风险评估中的应用研究[J].管理科学,2005,(4)：33-38
[120]石晓军.Logistic违约率模型最优样本配比与分界点的模拟分析[J].数理统计与管理,2006,(6)：675-682
[121]鲜文铎,向锐.基于混合Logit模型的财务困境预测研究[J].数量经济技术经济研究,2007,(9)：79-85
[122]彭建刚,易宇,李樟飞.商业银行贷款违约概率的测算方法探讨：贷款违约表法[J].管理学报,2008,(6)：12-18
[123]Krishan Nagpal, Reza Bahar. Measuring default correlation, Risk,2001,(3): 129-132
[124]华晓龙.基于宏观压力测试方法的商业银行体系信用风险评估[J].数量经济技术经济研究,2009,(4)：117-128
[125]韩岗.国外信用风险度量方法及其适用性研究[J].国际金融研究,2008,(3)：43-48.
[126]Martin D. Early warning of bank failure:a logit regression approach[J]. Journal of Banking and Finance,1977(3):249-276
[127]West David. Neural network credit scoring models [J]. Computer & Operations Research,2000,27(1):1131-1152
[128]彭建刚,屠海波,何婧,周颖辉.有序多分类logistic模型在违约概率测算的应用[J].财经理论与实践,2009,(4)：2-7
[129]卫海英.SPSS 10.0 for Windows在经济管理中的应用[M].北京：中国统计出版社,2003：248-271
[130]万柏坤,薛召,李佳等.应用roc曲线优选模式分类算法[J].自然科学进展,2006,(11)：1511-1516
[131]J.Williamson. Regional inequality and the proces of national development [J]. Economic Development and Culture Change,1965,(4):203-221
[132]于立勇,詹捷辉.基于Logistic回归分析的违约概率预测研究[J].财经研究,2004,(9)：69-75
[133]魏杰.30年中国对外开放战略的变革[J].理论前沿,2008,(10)：9-13
[134]钟伟,宛圆渊.预算软约束和金融危机理论的微观建构[J].经济研究,2001,(8)：44-52
[135]李文泓.关于宏观审慎监管框架下逆周期政策的探讨[J].金融研究,2009,59(7)：7-24
[136]王胜邦,陈颖.新资本协议内部评级法对宏观经济运行的影响：亲经济周期效应研究[J].金融研究,2008,(5)：48-64
[137]Repullo, R., Saurina, J. and Trucharte, C. Mitigating the procyclicality of Basel Ⅱ [A]. in Dewatripont, M., Freixas, X., and Portes, R., Macroeconomics Stability and Financial Regulation:Key issues for the G20[R], Center for Economic Policy Research, London, UK,2009:11-23
[138]Castren, Olli, Fitzpatrick, Trevor, Matthias Sydow. Assessing Portfolio Credit Risk Changes in a Sample of EU Large and Complex Banking Groups in Reaction to Macroeconomic Shocks[R]. ECB Working Paper No.1002.2009,(2): 78-86
[139]Piergiorgio Alessandri, Prasanna Gai, Sujit Kapadia, Nada Mora & Claus Puhr. Towards a framwork for quantifying systemic stability [J]. International Journal of Central Banking,2009,5 (9):47-81
[140]Fiori, Foglia and Iannotti. Beyond Macroeconomic risk:the role of contagion in the Italian corporate default correlation [C]. San Paolo:Annual Seminar on Banking, Financial stability and Risk,2008,(5):120-138
[141]Bank of Japan. The Framework for Macro Stress-Testing of Credit Risk: Incorporating Transition in Borrower Classifications[R], Financial System Report,2007(9):17-36