基于贝叶斯统计的金融市场若干风险测度分析
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摘要
近年来，随着我国金融服务业全球化以及利率市场化进程的不断推进，银行与证券业职能相互交融，市场规模迅速膨胀，金融风险结构日趋复杂，市场风险测量难度不断加大。金融创新进一步加剧了金融机构风险管理的难度。如何充分利用现有信息，准确地测度金融风险，进而对金融风险进行有效控制和管理，对于我国金融服务业的健康发展具有重大的理论意义和现实指导意义。
风险管理（Risk Management）是指对风险进行辩识、测量（包括预测）以及对风险优先处理次序进行排序的过程，通过对资源的调整和应用，实现对不利事件影响的最小化，并进行相应的监督和控制，从而达到规避风险的目的。金融风险则是指来自金融市场的不确定性所导致的风险。按照不确定性来源不同，金融风险粗略地可以划分为：市场风险、信用风险、操作风险和流动性风险等。
金融风险有效控制与管理的核心问题是如何对风险进行测量。现有的文献对如何测量金融风险提出了许多不同的风险测量方法和风险预测模型，但遗憾的是，近几年发生的重大金融事件，很少能够通过现有的模型加以预测。究其原因，主要是市场波动原因复杂，许多模型的假设条件得不到满足。另一个不可否认的原因是，任何一种统计方法或计量模型均存在一定的局限性。因此，探索新的、科学合理的统计模型来预测和防范可能遇到的风险，就显得十分必要。本文正是在上述背景下展开研究，探索贝叶斯统计对金融市场风险测度分析的模型方法，并据此对我国金融市场中的三类主要风险：市场风险、信用风险及操作风险进行实证分析，并针对我国金融市场风险成因进行分析，进而提出防范化解金融风险的对策建议。
本文基于贝叶斯统计对我国金融市场若干风险进行测度分析是金融风险测度分析方法上的研究与探索。主要内容及主要结论为：
第一部分（第2章），系统地分析了中国金融市场及市场风险构成、资本收益率分布特征，阐明市场风险、信用风险及操作风险的收益率分布均具有尖峰厚尾特征，详细介绍了测度金融市场若干风险的度量方法，尤其是对VaR的半参数法和极值理论的论述，为后续研究奠定理论基础。
第二部分（第3章），金融市场风险测度分析理论基础，在现有的模型假定基础上提出度量金融市场若干风险的贝叶斯模型假定，包括度量市场风险的GARCH-POT模型、度量信用风险的SW-GARCH-POT模型、度量操作风险的Weibull-POT模型，并从贝叶斯统计角度对模型的适用性进行论证，为后续风险度量提供量化工具。
第三部分（包括第4,5,6三章），在基础理论和模型方法的支持下对具体的金融市场风险进行实证分析。包括第4章贝叶斯GARCH-POT模型对股票市场风险、第5章贝叶斯SW-GARCH-POT模型对信用风险、第6章贝叶斯Weibull-POT模型对操作风险的实证分析。主要结论是，目前我国股票市场风险上证综指VaR0.99值为0.0354261，商业银行信用风险VaR0.99值为41.11，操作风险VaR0.99值为3934.43；同一置信水平下贝叶斯统计方法测算的VaR较经典统计方法测算的VaR值偏大；贝叶斯模型方法解决了现有模型方法信用调级的繁琐以及金融市场风险度量损失数据缺失问题；贝叶斯风险测度分析方法具有一定的科学性和借鉴性。
第四部分（包括第7、8两章），对金融市场若干风险的主要成因进行分析，得出市场机制不健全和政策性因素是影响各类风险的主要原因；并提出防范化解金融市场风险的对策建议。
论文研究主要采用理论分析和实证分析相结合、定性分析和定量分析相结合的研究方法，尤其注重贝叶斯统计方法的运用。理论分析综合运用了金融学、金融统计学、计量经济学、贝叶斯统计学的相关理论。实证分析以贝叶斯方法为主。贝叶斯方法的应用，一弥补了现有传统度量方法低估风险的不足，二解决了金融风险分析过程中数据缺失问题。模型中参数估计采用MCMC方法中的Gibbs抽样；数值分析和实证分析的计算采用WinBUGS软件、SAS软件以及Eviews5.1、6.0软件。
论文主要创新点体现在：
1.股票市场风险度量中的贝叶斯GARCH-POT模型应用属于首创。股市风险测度的贝叶斯分析方法弥补了经典统计方法低估市场风险的不足。
2.贝叶斯SW-GARCH-POT模型对商业银行信用风险测度分析是信用风险测度方法上的探索。将信用等级的跃迁以转移变量的形式引入SW-GARCH模型实现对信用风险的测算是一种新的尝试。
3.对商业银行操作风险的测度分析中，Weibull分布选择有别于现有其他研究成果。实证结果表明，风险测度的贝叶斯分析方法是可行的，具有可操作性。
4.系统应用贝叶斯统计方法度量金融风险在国内还不多见，本文研究为风险度量提供了方法上的选择。在一定意义上弥补国内该领域的欠缺。
论文不足之处：
1.由于时间所限，金融市场流动性风险未作出分析，有待于后续进一步研究。
2.实证分析只测算了风险的VaR值，未对防范风险应配置相应的经济资本作出估算，有待于进一步完善。
3.由于本人掌握的知识有限，先验分布的选取借鉴于前人已有的研究成果，这本身就是贝叶斯统计的难点所在，也是本人的研究努力方向。
In the past few years, the globalization of financial service industry and thepromotion of the interest rate liberation have been blending the functions of banks andsecurities, enlarging the market size, complicating the financial risk structure andarousing difficulties of measuring market risks. The difficulties are intensified offinancial agencies managing risks by financial creation. Therefore, significant in boththeory and reality to the development of financial service industry is how to make fulluse of available information to measure the financial risks, and then to make an effectivecontrol and management of financial risks.
Risk management is a process, in which the risks are identified, measured andpredicted, and arranged in the order of priority, and is aimed to minimize the unfavorableeffect in the way of adapting and applying the resources, make proper supervision andmanagement, and avoid the risks. Financial risks arises from the uncertainty of financialmarket, and due to the different sources, they are generally classified into the followinggroups: market risks, credit risks, operational risks, liquidity risks and so on.
The critical step in the effective control and management of financial risks is how tomeasure the risks. Although the available documents offer to measure risks in manydifferent approaches and models, recent important financial crises have rarely beenforecast in the models available. On the one hand, the intricate causes behind the marketups and downs are not easy to be found, and therefore, the assumptions of the models cannot be met; on the other hand, the undeniable is that any statistic approach or measuringmodel exists some limitations. Hence, it is necessary to explore new and scientificstatistic models to predict and avoid the potential risks. In the above background, thethesis is to explore the model of Bayesian statistics measuring the risks in financialmarket, on the basis of which three kinds of risks are to be analyzed, namely, marketrisks, credit risks, and operational risks, to analyze the causes of the risks in China’sfinancial market, and to advance the suggestions and strategies to avoid and solve therisks.
The thesis, composed of four parts, measuring and analyzing the risks in China’sfinancial market, is to study and explore the financial risk-assessing-and-analyzing approaches.
In Chapter Two, as the first part of the thesis, a systematic analysis is made aboutthe construction of China’s financial market, the characteristics of diverse risks infinancial market, and the features of yield rate distribution; an interpretation is made thatany yield rate distribution of the three, such as the market risks, the credit risks, and theoperational risks, is characterized by a steep kurtosis and a buffering skewness, butbuffers in a different direction. Besides, the theories are introduced about how to assessthe risks in financial market, and the semi-parameter based on EGACH-VAR andExtreme Value theory are specifically illustrated, which bases the succeeding study.
In Chapter Three, as the second part, the Bayesian Hypothesis is advanced to assessrisks in financial market on the base of existing model hypothesis, such as BayesianGARCH-POT model to assess market risks, Bayesian SW-GARCH-POT model to assessoperational risks; the applicability of the model is illustrated from the Bayesian Statisticpoint of view, to offer the quantizing tool for the succeeding risks.
In Chapter Four, Chapter Five and Chapter Six, as the third part, the empiricalanalyses of the specific financial market risks are made based on the fundamentaltheories and models, including the analyses of stock market risks by BayesianGARCH-POT model in Chapter4, the analyses of credit risks by BayesianSW-GARCH-POT model in Chapter5, and the analyses of operational risks by BayesianWeibull-pot model. A conclusion come from these analyses that the VaR of the risks ofChina’s stock market is0.0041, of China’s commercial banks is0.056, and of operationalrisks is0,032. The problem is solved of the complexity of the existing model adjustingthe grade of credit and of the loss of data, and meanwhile, the problem of data invalidityis avoided aroused by the traditional measuring approach.
In Chapter Seven and Chapter Eight, as the part four, the main causes of the risks ofChina’s financial market are analyzed on the base of empirical results, and the conclusionis drawn that the imperfect market mechanism and policy factors are the main elementsto influence the specific market risks.
Methodology:
The thesis is to adopt the approach of the combination of theory analysis andempirical test, and the combination of qualitative analysis and quantitative analysis, andespecially focus on the application of Bayesian Statistics. In the theory analysis, the theories of finance, finance-statistics, econometrics and Bayesian Statistics are applied ina comprehensive way. Empirical analysis mainly adopt the Bayesian Statistics, whichovercomes the disadvantage of traditional measuring approach underestimating the risks,and solves the problem of loss of data in measuring the financial risks. The application ofGibbs sampling works out the problem of Bayesian Statistics financial risks in thecomputing process.
The analyses made in the thesis are favored by the WinBUGS software,SASsoftware and Eview5.1,6.0software.
Innovations:
First, Bayesian GARCH-POT model is applied to assess the risks of stock marketfor the first time, perfecting the classic statistics in underestimating the market risks.
Second, the empirical analysis made by Bayesian SW-GARCH-POT model ofcommercial banks’ credit risks explores the credit risks. The new attempt is to analyze, inthe form of variables, the credit risks.
Third, in the analyses of commercial banks’ operational risks, the results with thechoice of Weibull distribution differs from those with others.
The study shows that it is feasible and operational to measure risks by means ofBayesian Statistics of Weibull.
Fourth, rarely seen is to apply Bayesian Statistics to the analyses of financial marketrisks, and therefore, the thesis is to offer the choices of approaches to assess risks, which,in a way, better the research conditions of this field.
The thesis needs to be improved in the following:
First, the shortage of time leads to the fact that the liquidity risks in the financialmarket are not analyzed, which will be further studied in the future research.
Second, the empirical study only measures VaR, and the evaluation is not made ofcapital that is used to avoid risks, and this will be perfected in the further study.
Third, the difficulty, consisting of the accumulation of the knowledge and thereference to the previous academic study in the choice of empirical distribution, is thefurther study in the future.
风险管理（Risk Management）是指对风险进行辩识、测量（包括预测）以及对风险优先处理次序进行排序的过程，通过对资源的调整和应用，实现对不利事件影响的最小化，并进行相应的监督和控制，从而达到规避风险的目的。金融风险则是指来自金融市场的不确定性所导致的风险。按照不确定性来源不同，金融风险粗略地可以划分为：市场风险、信用风险、操作风险和流动性风险等。
金融风险有效控制与管理的核心问题是如何对风险进行测量。现有的文献对如何测量金融风险提出了许多不同的风险测量方法和风险预测模型，但遗憾的是，近几年发生的重大金融事件，很少能够通过现有的模型加以预测。究其原因，主要是市场波动原因复杂，许多模型的假设条件得不到满足。另一个不可否认的原因是，任何一种统计方法或计量模型均存在一定的局限性。因此，探索新的、科学合理的统计模型来预测和防范可能遇到的风险，就显得十分必要。本文正是在上述背景下展开研究，探索贝叶斯统计对金融市场风险测度分析的模型方法，并据此对我国金融市场中的三类主要风险：市场风险、信用风险及操作风险进行实证分析，并针对我国金融市场风险成因进行分析，进而提出防范化解金融风险的对策建议。
本文基于贝叶斯统计对我国金融市场若干风险进行测度分析是金融风险测度分析方法上的研究与探索。主要内容及主要结论为：
第一部分（第2章），系统地分析了中国金融市场及市场风险构成、资本收益率分布特征，阐明市场风险、信用风险及操作风险的收益率分布均具有尖峰厚尾特征，详细介绍了测度金融市场若干风险的度量方法，尤其是对VaR的半参数法和极值理论的论述，为后续研究奠定理论基础。
第二部分（第3章），金融市场风险测度分析理论基础，在现有的模型假定基础上提出度量金融市场若干风险的贝叶斯模型假定，包括度量市场风险的GARCH-POT模型、度量信用风险的SW-GARCH-POT模型、度量操作风险的Weibull-POT模型，并从贝叶斯统计角度对模型的适用性进行论证，为后续风险度量提供量化工具。
第三部分（包括第4,5,6三章），在基础理论和模型方法的支持下对具体的金融市场风险进行实证分析。包括第4章贝叶斯GARCH-POT模型对股票市场风险、第5章贝叶斯SW-GARCH-POT模型对信用风险、第6章贝叶斯Weibull-POT模型对操作风险的实证分析。主要结论是，目前我国股票市场风险上证综指VaR0.99值为0.0354261，商业银行信用风险VaR0.99值为41.11，操作风险VaR0.99值为3934.43；同一置信水平下贝叶斯统计方法测算的VaR较经典统计方法测算的VaR值偏大；贝叶斯模型方法解决了现有模型方法信用调级的繁琐以及金融市场风险度量损失数据缺失问题；贝叶斯风险测度分析方法具有一定的科学性和借鉴性。
第四部分（包括第7、8两章），对金融市场若干风险的主要成因进行分析，得出市场机制不健全和政策性因素是影响各类风险的主要原因；并提出防范化解金融市场风险的对策建议。
论文研究主要采用理论分析和实证分析相结合、定性分析和定量分析相结合的研究方法，尤其注重贝叶斯统计方法的运用。理论分析综合运用了金融学、金融统计学、计量经济学、贝叶斯统计学的相关理论。实证分析以贝叶斯方法为主。贝叶斯方法的应用，一弥补了现有传统度量方法低估风险的不足，二解决了金融风险分析过程中数据缺失问题。模型中参数估计采用MCMC方法中的Gibbs抽样；数值分析和实证分析的计算采用WinBUGS软件、SAS软件以及Eviews5.1、6.0软件。
论文主要创新点体现在：
1.股票市场风险度量中的贝叶斯GARCH-POT模型应用属于首创。股市风险测度的贝叶斯分析方法弥补了经典统计方法低估市场风险的不足。
2.贝叶斯SW-GARCH-POT模型对商业银行信用风险测度分析是信用风险测度方法上的探索。将信用等级的跃迁以转移变量的形式引入SW-GARCH模型实现对信用风险的测算是一种新的尝试。
3.对商业银行操作风险的测度分析中，Weibull分布选择有别于现有其他研究成果。实证结果表明，风险测度的贝叶斯分析方法是可行的，具有可操作性。
4.系统应用贝叶斯统计方法度量金融风险在国内还不多见，本文研究为风险度量提供了方法上的选择。在一定意义上弥补国内该领域的欠缺。
论文不足之处：
1.由于时间所限，金融市场流动性风险未作出分析，有待于后续进一步研究。
2.实证分析只测算了风险的VaR值，未对防范风险应配置相应的经济资本作出估算，有待于进一步完善。
3.由于本人掌握的知识有限，先验分布的选取借鉴于前人已有的研究成果，这本身就是贝叶斯统计的难点所在，也是本人的研究努力方向。
In the past few years, the globalization of financial service industry and thepromotion of the interest rate liberation have been blending the functions of banks andsecurities, enlarging the market size, complicating the financial risk structure andarousing difficulties of measuring market risks. The difficulties are intensified offinancial agencies managing risks by financial creation. Therefore, significant in boththeory and reality to the development of financial service industry is how to make fulluse of available information to measure the financial risks, and then to make an effectivecontrol and management of financial risks.
Risk management is a process, in which the risks are identified, measured andpredicted, and arranged in the order of priority, and is aimed to minimize the unfavorableeffect in the way of adapting and applying the resources, make proper supervision andmanagement, and avoid the risks. Financial risks arises from the uncertainty of financialmarket, and due to the different sources, they are generally classified into the followinggroups: market risks, credit risks, operational risks, liquidity risks and so on.
The critical step in the effective control and management of financial risks is how tomeasure the risks. Although the available documents offer to measure risks in manydifferent approaches and models, recent important financial crises have rarely beenforecast in the models available. On the one hand, the intricate causes behind the marketups and downs are not easy to be found, and therefore, the assumptions of the models cannot be met; on the other hand, the undeniable is that any statistic approach or measuringmodel exists some limitations. Hence, it is necessary to explore new and scientificstatistic models to predict and avoid the potential risks. In the above background, thethesis is to explore the model of Bayesian statistics measuring the risks in financialmarket, on the basis of which three kinds of risks are to be analyzed, namely, marketrisks, credit risks, and operational risks, to analyze the causes of the risks in China’sfinancial market, and to advance the suggestions and strategies to avoid and solve therisks.
The thesis, composed of four parts, measuring and analyzing the risks in China’sfinancial market, is to study and explore the financial risk-assessing-and-analyzing approaches.
In Chapter Two, as the first part of the thesis, a systematic analysis is made aboutthe construction of China’s financial market, the characteristics of diverse risks infinancial market, and the features of yield rate distribution; an interpretation is made thatany yield rate distribution of the three, such as the market risks, the credit risks, and theoperational risks, is characterized by a steep kurtosis and a buffering skewness, butbuffers in a different direction. Besides, the theories are introduced about how to assessthe risks in financial market, and the semi-parameter based on EGACH-VAR andExtreme Value theory are specifically illustrated, which bases the succeeding study.
In Chapter Three, as the second part, the Bayesian Hypothesis is advanced to assessrisks in financial market on the base of existing model hypothesis, such as BayesianGARCH-POT model to assess market risks, Bayesian SW-GARCH-POT model to assessoperational risks; the applicability of the model is illustrated from the Bayesian Statisticpoint of view, to offer the quantizing tool for the succeeding risks.
In Chapter Four, Chapter Five and Chapter Six, as the third part, the empiricalanalyses of the specific financial market risks are made based on the fundamentaltheories and models, including the analyses of stock market risks by BayesianGARCH-POT model in Chapter4, the analyses of credit risks by BayesianSW-GARCH-POT model in Chapter5, and the analyses of operational risks by BayesianWeibull-pot model. A conclusion come from these analyses that the VaR of the risks ofChina’s stock market is0.0041, of China’s commercial banks is0.056, and of operationalrisks is0,032. The problem is solved of the complexity of the existing model adjustingthe grade of credit and of the loss of data, and meanwhile, the problem of data invalidityis avoided aroused by the traditional measuring approach.
In Chapter Seven and Chapter Eight, as the part four, the main causes of the risks ofChina’s financial market are analyzed on the base of empirical results, and the conclusionis drawn that the imperfect market mechanism and policy factors are the main elementsto influence the specific market risks.
Methodology:
The thesis is to adopt the approach of the combination of theory analysis andempirical test, and the combination of qualitative analysis and quantitative analysis, andespecially focus on the application of Bayesian Statistics. In the theory analysis, the theories of finance, finance-statistics, econometrics and Bayesian Statistics are applied ina comprehensive way. Empirical analysis mainly adopt the Bayesian Statistics, whichovercomes the disadvantage of traditional measuring approach underestimating the risks,and solves the problem of loss of data in measuring the financial risks. The application ofGibbs sampling works out the problem of Bayesian Statistics financial risks in thecomputing process.
The analyses made in the thesis are favored by the WinBUGS software,SASsoftware and Eview5.1,6.0software.
Innovations:
First, Bayesian GARCH-POT model is applied to assess the risks of stock marketfor the first time, perfecting the classic statistics in underestimating the market risks.
Second, the empirical analysis made by Bayesian SW-GARCH-POT model ofcommercial banks’ credit risks explores the credit risks. The new attempt is to analyze, inthe form of variables, the credit risks.
Third, in the analyses of commercial banks’ operational risks, the results with thechoice of Weibull distribution differs from those with others.
The study shows that it is feasible and operational to measure risks by means ofBayesian Statistics of Weibull.
Fourth, rarely seen is to apply Bayesian Statistics to the analyses of financial marketrisks, and therefore, the thesis is to offer the choices of approaches to assess risks, which,in a way, better the research conditions of this field.
The thesis needs to be improved in the following:
First, the shortage of time leads to the fact that the liquidity risks in the financialmarket are not analyzed, which will be further studied in the future research.
Second, the empirical study only measures VaR, and the evaluation is not made ofcapital that is used to avoid risks, and this will be perfected in the further study.
Third, the difficulty, consisting of the accumulation of the knowledge and thereference to the previous academic study in the choice of empirical distribution, is thefurther study in the future.
引文
①Markov链的定义见茆诗松《高等数理统计》高等教育出版社2006年第2版441页
②转移核的定义见茆诗松《高等数理统计》高等教育出版社2006年第2版442页
①桂文林数量经济技术经济研究2010第一期，107-118.
①茆诗松贝叶斯统计中国统计出版社2008第20页朱慧明贝叶斯统计计量经济模型科学出版社2009
①周艳菊．基于Bayesian-Copula方法的商业银行操作风险度量[C]．中国管理科学．2011．17-25
①王灵芝．中国证券市场流动性风险的量化与管理研究．2010．
①林静．基于MCMC的贝叶斯生存分析理论及其在可靠性评估中的应用[C]经济与管理科学籍．2008．
②王茹梦雪中国股票市场风险问题及化解思路[C]经济研究参考2012第45期55-63。
[1] A.D. Gro，F.C. Harmantzis, GJ. Kaple. Operational Risk and Reference Data:Exploring Costs, Capital Requirements and Risk Mitigation. November,2005.URL:http://papers.ssrn.com/so13.
[2] A.J. McNeil. Sampling nested Archimedean copulas[J]. Journal of StatisticalComputation and Simulation,2008,78(6):567.
[3] Acerbi,C.,Tasche D.,Expected Shortfall:a natural coherent alternative to Value atRisk[J].Economics Notes,2002,31(2),379-388.
[4] Ahman, E.I. and D.L. Kao. Rating Drift of High Yield Bonds[J]. Journal ofFixedIncome,1992,3:15-20.
[5] Albert, J. Bayesian Computation with R[M]. New York: Springer,2007.
[6] Altman.E.I, Haldeman R G&Narayanan.ZETA Analysis:A New Model to IdentifyBankruptcyRisk of Corporations[J].Journal of Banking&Finance,1977(1),29-54.
[7] Altman.E.I,Financial Ratios,Discriminate Analysis and the Prediction of CorporateBankruptcy [J].Journal of Finance1968,23(9),589-609.
[8] Altman.E.L,Credit Risk Measurement:Developments Over the last20Years[J].Journal of Banking And Finance1997(21).
[9] Ariane Chapelle Yves Crama Georges Hubner, Jean-Philippe Peters. Measuring andManaging Operational Risk in the Financial Sector: An Integrated Framework.February,2005.URL: http://papers.ssrn.com/so13.
[10] Ashby A J, Leon R V, Thyagarajan J. Bayesian Modeling of Accelerated Life Testswith Random Effects[J]. http://stat.bus.utk.edu/techrpts/2003/2003-Ol.pdf.
[11] Barclay G. Techniques of Population Analysis[M]. New York: Wiley.1958.
[12] Basel Committee on Banning Supervision, InternationalMeasurement andCapital Standards, A Revised Framework[R].http://www.bis.org/publ/bcbs118. pdfConvergence of Capital Updated November,2005.
[13] Basel II: International Convergence of Capital Measurement and Capital Standards:aRevised Framework[R],Basel Committee Publications, June2004. http://papers.ssrn. com/so13.
[14] Bauwens L., Lubrano M.. Bayesian inference on GARCH models using the Gibbssampler[J].The Econometrics Journal,1998(1).
[15] Bayes T. An essay towards solving a problem in the doctrine of chances[J].Reprinted in Biometrilca，1958(2).
[16] Beare, B. Copula-based mixing conditions for Markov chains[D], mimeo，University of Oxford,2007.
[17] Berger J O，贾乃光译.统计决策理论和贝叶斯分析[M]，北京：中国统计出版社，1998.
[18] Berger J. O.. Bayesian Analysis: A Look at Today and Thoughts of Tomorrow[J].Journal of The American Statistical Association,2000,95(30), pp:1269-1276.
[19] Berger, J.O. Statistical Decision Theory and Bayesian Analysis[M]. New York:Springer-Verlag,1985.
[20] Berger, J., Ghosh, J.K., Mulchopadhyay, N. Approximations to the Bayes Factor inModel Selection Problems and Consistency Issues[J]. Journal of Statistical Planningand Inference,2003(112):241-258.
[21] Box G E P, Tiao G C. Bayesian inference in statistical analysis[M]. NewYork: JohnWiley and Sons，1992.
[22] Bulter J W. Machine sampling from give probabolity distrbutions.In:Symposium onmonte Carlo Methods[M].edited by Meyer M A, New york:Wiley,1958.
[23] Chiara Cornalba, Paolo Giudici.Statistical models for operational risk management
[M]. Sica A.2004,338:166-172.
[24] Chris Maten.Managing bank capital Capital Allocation and PerformanceMeasurement[M]. Second edition Wiley,2000.
[25] Christian Locher, Jens I. Mehlau, Oliver Wild. Towards risk adjusted controlling ofstrategic IS projects in banks in the light of Basel11[M].annual Hawaii internationalconference on system sciences，2004.
[26] Congdon P Applied Bayesian Modelling[M].England: John Wiley and Sons.2003.
[27] Congdon P Bayesian Statistical Modelling[M].England: John Wiley and Sons，2001.
[28] Corradi V.and.R.Swanson. Predictive Density Evaluation, in Handbook ofEconomic Forecasting,G.Elliott,C.W.J.Granger and A.Timmermann[J].ed.s, NorthHolland, msterdam.2005.
[29] Cox D R, Oakes D. Analysis of Survival Data.[M]. New York: Chapman andHa11.1984.
[30] Credit Suisse. Credit Risk+: A Credit Risk Management Framework [R]. CreditSuisse Financial Products,1997
[31] Credit Suisse.Credit Risk+: A Credit Risk Management Framework[R].Credit SuisseFinancial Products,1997.http://u.csfb.com/institutional/research/assets/creditrisk.pdf.
[32] CreditMetrics[R].Technical Document, JPMorgan，1997.
[33] Crosbie, P.J. and J.R. Bohn Modeling default risk[D] KMV working paper，2002.
[34] Dempster A P,Laird N,Rubin D B.Maximam likelihood estimation from incompletedata via the EM algorithm (with discussion).J Roy Statist1977.
[35] Duffie D. and K. Singleton Modeling term structures of default able bonds.CreditRisk:Princing, Measurement,and Management. Princeton University PressEricRosenberg,2003.
[36] Embrechts, P., Furrer, H., Kaufmann, R., Quantifying regulatory capital foroperational risk, Derivatives Use [J]. Trading&Re gulation.2003(9):217-233.
[37] Fisher, W.D.Estimation in the Linear Decision Model[J].International EconomicRcview,1962(3)：1-29.
[38] Fotios C. Harmantzis. Operational Risk Management in Financial Services and theNe+Basel Accord. URL[D]: http://papers.ssrn.com/so13
[39] Fotios C. Harmantzis. Operational Risk Management in Financial Services and theNew Basel Accord[D].URL: http://papers.ssrn.com/so13.
[40] Frachot A，Georges P. and Roncalli T.Loss distribution approach for operational risk,Group de Recherche Operationnelle[R], Credit Agricole, dance www. gloriamundi.org,2001.
[41] Frachot A.Georges erational riskGroup www.gloriamundi.org,Pde and R.oncalli TLoss distribution Recherche Operationnelle[M], Credit approach Agricole,forop-France，2001.
[42] Frachot, A，Roncalli, T. and Salomon, E.The correlation problem in op-erational risk.Groupde Recherche Operationnelle[D], Credit Agricole, dance, http://www.gloriamundi.org/picsresources/aftres.pdf,2004.
[43] Frances P. H., Hoke H., Pap R. Bayesian analysis of seasonal unit root and meansshifts[J].Journal of Econometrics,1997(3), pp:359-380.
[44] Gelfand A E,Smith A F M. Gibbs sampling for marginal posterior expections[J].Communications in Statistics A,1991.
[45] Gelfand A E,Smith A F M.Sampling based approaches to calculating marginaldensities [J].J Amer Statist Assoc,1990.
[46] Gelman A, Carlin J B, Stern H S, Rubin D B. Bayesian Data Analysis[M].NewYork：Chapman andHaIUCRC，2004.
[47] Gelman A,Carlin J B,Sten H S,Bubin D B.Bayesian data analysis[J].New York：Chapman Hall,1995.
[48] Gilks W R, Richardson S. Spiegelhalter D J. Markov Chains Monte Carlo inpractice.Chapman and Hall,1996.
[49] Gunther Helbok, Christian Wagner. Determinants of Operational Risk Reporting inthe Banking Industry[D]. December23,2005. URL: http://papers.ssrn.com/so13
[50] Hasting W K. Monte Carlo sampling methods using Markov Chains and theirapplications[M].Biometrika,1970.
[51] Hayakawa丫Irony T, Min X. System and Bayesian Reliability[M]. Singapore:World Scientific Publishing.2001
[52] Hildreth, C.Bayesian Statisicians and Remote Clients[J] Econometrics,1963（31）：422-438.
[53] Hoek H., Lucas A., Van Dijk H. K.. Classical and Bayesian aspects of robust unitroot inference[J]. Journal of Econometrics,1995,69(1), pp:27-59.
[54] Hosmer D, Lemeshow S. Applied Survival Analysis[M]. New York: Wiley,1999.
[55] Hougaard P.Analysis of Multivariate Survival Data[M]. New York: Springer-Verlag,2000.
[56] Ilkka K. Methods and problems of software reliability estimation[J]. URL: http://www.vtt.fi/publications/index.jsp.Journal of Derivatives,1995,3(2):73-84.293-15
[57] Jouanin, J.F Rapuch, G, Riboulet, G.&Roncalli, T. Modelling Dependence forCredit[S],2001.
[58] JPMorgan. RiskMetrics Technical Document[M].3rd.New York: JPMorgan.1996.
[59] K. Aas, C.and et Czado. Pair-copula constructions of multiple dependence[J].Mathematics and Economics,2007,02(001):23-24.
[60] Kadam,Migration.A，. Bayesian Inference for Issuer Heterogeneity in Credit RatingJournal of Banking and Finance[J], Lenk,2008(32): P2267-2274.
[61] Kadam,Migration.A., Lenk, P. Bayesian Inference for Issuer Heterogeneity in CreditRating[J].Journal of Banking and Finance,2008(32):2267-2274.
[62] Kasuya M, Tanemura T. Small scale Bayesian VAR modeling logistic diffusion.Journal of Forecasting,2002,20(2):246-253.
[63] KMV Corporation.Credit Monitor Overview [R].SanFrancisco California,1993.
[64] Lawless J F. Statistical Models and Method for Lifetime Data[M]. New York: JohnWiley&Sons,1982.
[65] Li, D.X.(2000) On default correlation: a copula function approach[J]. FixedIncome,943-54.
[66] Lindley D V "Approximate Bayesian methods" in Bayesian Statistics[MJ. Valencia,Spain: Valencia Press.1980
[67] Litterman R B. Forecasting with Bayesian vector auto-regressions: Five years ofexperience [J]. Journal of Business and Economic Statisties.1986,4(1), pp:5-15.
[68] Lubrano M.. Testing for unit roots in a Bayesian framework[J]. Journal ofEconomtrics,1995,69{1), pp:81-109.
[69] Makov U E, Smith A F M, Liu Y H. Bayesian Methods in Actuarial Science[J].'heStatistician.1996
[70] Martz H F, Waller R A. Bayesian reliability analysis[M]. New York: JohnWiley&Son.1982
[71] Masson.PaulContagion:MonsoonalEffects,Spillovers,and Jumps between MultipleEquilibria,IMF(working paper)1998,I42.
[72] McNeil A. Embrechts, P., D. Straumann. Correlation pitfalls and alternatives[J].Risk London Risk Magazine Limited,1999,12:69-71.
[73] McNeil, A, Wendin, J. Dependent Credit Migrations[J]. Journal of Credit Risk,2006,(2):87-114.
[74] McNeil, A., Wendin, J. Bayesian Inference for Generalized Linear Mixed Models ofPortfolio Credit Risk[J]. Journal of Empirical Finance,2007,(l4):131-149.
[75] Monahan J. F.. Fully Bayesian analysis of ARMA timeEconometries, Serves models[J]. Journal of1983,21(2), pp:307-331.
[76] Mori T., Harada E. Internal Risk Based Approach-Evolutionary Approaches toRegulatory Capital Charge for Operational Risk [Z]. Working Paper. Bank of Japan.2000.
[77] P Artzner, F. Delbaen. Default Risk insurance and incomplete markets[J].Mathematical Finance,1995,5(3):187-195.
[78] Pai J.Bayesian Analysis of Compound Loss Distributions[J]. Journal ofEconometrics.1997(3).
[79] Peter B, McCullouch R,Rossi P. Account-level Modeling for Promotion, AnApplication of a constrained Parameter Hierarchical Model[J]. Journal oftheAmerican StatisticaAssociation.1999,94:1063-1073
[80] Phillips P. C. B.Bayes models and forecasts of Australian macroeconomic timeseries[J]. Cowles Foundation Paper987,1994, pp:53-86.
[81] Pickands J. Statistical inference using extreme order statistics[J].Annals ofStatistics,1975(3),119-131.
[82] Press S J. Bayesian Statistics, Principle, models and applications[M]. New York:Wiley.1991
[83] Qin, D. Bayesian Econometrics The First Twenty Years[J]. Econometric Theory,1996(12):500-516.
[84] Quantitive methods in credit management: A survey. Operation Eric Bouye, V D.,Ashkan Nikeghbali,Gael Riboulet,Thierry Roncalli, Copulas:an open field forrisk management. Electronic copy,2001.available at: http://ssrn. com/abstract=1032557,2-8[J]. Survey,July-August,1994
[85] R. Litterman, T. Iben. bond valuation and the term structure of credit spreads[J].Corporate Journal of Portfolio Management,1991(Spring).
[86] Rangan Gupta. Forecasting the South African economy with VARs and VECMs [J].South African Journal of Economics,2006(74), pp:611-628.
[87] Robinson D G. A Hierarchical Bayes Approach to System Reliability Analysis[R].Sandia National Laboratories.USA.2001
[88] Rosenberg, J. V., Schuermann,T., A general approach to integrated risk managementwith skewed, fat-tailed risks[J].Journal of Banking and Finance,2006,(8),2517-2534.
[89] Rosenberg, J.V, Schuermann,T.A general approach to integrated risk managementwith skewed, fat-tailed risks[J].Journal of Financial Economics,2006,79(3):569-614
[90] Rubinstein R Y.Simulation and Monte Carlo methods[M]. New York:John Wiley-Sons,1981.
[91] Sandor, Zsolt, Michel WedeL. Designing Conjoint Choice Experiments UsingManagers' Prior Beliefs[J]. Journal of Marketing Research,2001(28):430-444.
[92] Schotman P C, Van Dijk H. K. A Bayesian analysis of the unit root hypothesis in realexchange rates[J]. Journal of Econometrics,1991(6), pp:195-238.
[93] Sehotman P. C, Van Dijk H. K. On Bayesian routes to unit roots[J]. Journal ofApplied Econometrics,1991(6), pp:387-401.
[94] Sims C. A. Bayesian skepticism on unit root econometrics[J]. Journal of EconomiesDynamics and Control,1988(4), pp:463-474.
[95] Sims C. A., T. Z Bayesian methods for dynamic multivariate models[J]. InternationalEconomic Review,1998,39(4), pp:949-968.
[96] Sinha D, Ibrahim1q Chen M H. Bayesian models for survival data from cancerprevention studies[R].Department of Mathematical Sciences, Worcester PolytechnicInstitute.2000.
[97] Sklar. Fonctions derartition a n dimensions etleurs marges[J]. Statistics UnivParis1959,8:229-231.
[98] Spiegelhalter, D.J., Best, N.G., Carlin, B.P., Van der Linde. A Bayesian Measures ofModel Complexity and Fit[J]. Journal of the Royal Statistical Society Series B,2002,64(3):583-639
[99] Spiegelhalter, D.J.et al. WinBUGS Version1.4User Manual[M]. MRCBiostatistics Unit, Cambridge,2003.
[100] Stefanescu, C., Tunaru, R., Turnbull, S. The Credit Rating Process and Estimationof Transition Probabilities A Bayesian Approach[J]. Journal of Empirical Finance,2009(16):216-234.
[101] Therneau T, Grambsch P. Modeling Survival Data[M]. New York: Springer,2000
[102] Uhlig H. Bayesian vector auto-regressions with stochastic volatility[M].Econometrica,1997.
[103] Vicente N A,Jesus O. Alternative regression models for censored data from the PHmodel to themodifiedsemiparametricAFTmodel[J]. http://www.wiwi.uni-bielefeld.dehkauermann/survivaVnunez-anton.pdf65(1), pp:59-73.
[104] Wald A，王福宝译.统计决策函数[M].上海：上海教育出版社，1963.
[105] West M., Harrison J. Bayesian forecasting and dynamic models[J].2ndedition.NewYork: Springer,1997.
[106] Wilson.T.Portfolio credit risk[R]. Risk,1997,10(9).
[107] Wu C F J.On the convergence properties of the EM algorithm[J].in:The Annals ofStatistics,198.
[108] Ze11ner A，Min C.Gibbs sampler convergence criteria [J]. Journal of Americanstatistical association,1995(2), pp:763-778.
[109] Zellner A. Bayesian estimation and Prediction using asymmetric loss functions [J].Journal ofAmerican Statistical Association,1986,81(2), pp:446-451.
[110] Zellner A. On the Bayesian estimation of multivariate regression [J]. Journal ofroyal statistical society B,1964,26(2), pp:277-285.
[111] Zellner A., Chong C. Forecasting international growth rates using Bayesianshrinkage and other Procedures[J]. Journal of Econometries,1989,40(2), pp:183-202.
[112] Zellner, A. and Tiao, G. C.Bayesian Analysis of the Region Modei withAutocarelated Errors [J].1. of the American Statistical Association.1964(59),pp.763-778.
[113][美]克里斯．莫里森金融风险度量概论[M]北京：清华大学出版社2008.
[114] Kotzs，吴喜之．现代贝叶斯统计学[M]．北京：中国统计出版社，2000
[115]毕文杰．基于粗糙集与贝叶斯理论的不确定信息群决策方法研究[D]．博士学位论文，中南大学，2008.
[116]薄纯林，王宗军．基于贝叶斯网络的商业银行操作风险管理[J]．金融理论与实践，2008（4）
[117]陈珂．基于Bayes估计与极值理论的VaR研究[D]．硕士学位论文，重庆大学，2009．
[118]陈公越，于盟．金融风险测量和全面风险管理[M]上海：上海科学技术出版社，2011
[119]陈学华，杨辉耀，黄向阳．POT模型在商业银行操作风险度量中的应用[J]．管理科学，2003（1）
[120]程功等．信息噪音、结构化模型与银行违约概率度量[[J]．管理科学学报，2007（4）：38-48
[121]程建，连玉君，刘奋军．信用风险模型的贝叶斯改进研究[J]．国际金融研究，2009（1）：63-68
[122]丁东洋．信用风险分析中贝叶斯方法及其应用研究[D]．博士学位论文，天津财经大学，2009．
[123]董磊磊．基于贝叶斯网络的突发事件链建模研究[D]．硕士学位论文，大连理工，2009．
[124]樊欣，杨晓光．中国商业银行也操作风险分析[R]． CFEF研究报告，RR/03/09,2003
[125]方洪全，曾勇．多标准等级判别模型在银行信用风险评估中的应用研究[J]．金融研究2004（9）：92-100
[126]冯静，刘琦，周经纶等．相关函数融合法及其在可靠性分析中的应用．[J]．系统工程与电子技术．2003（2）
[127]高丽君，李建平，徐伟宣，王书平．基于POT方法的商业银行操作风险极端值估计[J]．运筹与管理，2007（1）：1-6
[128]高丽君，李建平等．操作风险度量模型与方法研究[[J]．管理评论．Vo1．18No．9（2006）：8-18．125
[129]郭百钢．基于Bayes网络的投资项目评估与决策方法研究[D]南京理工大学2004．
[130]侯成琪，王频．基于连接函数的整合风险度量研究[J]．统计研究，2008,25（11）72-80
[131]胡素华．连续时间资产收益模型的贝叶斯分析[D]．博士学位论文，天津大学，2007．
[132]金浩．经济统计分析与SAS应用[M]．北京：经济科学出版社2002.
[133]孔丽娜． ARCH模型族的贝叶斯分析及其在经济中的应用[D]．博士学位论文，暨南大学．中国全文数据库经济与管理科学辑，2009（9）
[134]黎子良，邢海鹏，金融市场中的统计模型和方法[M]北京：高等教育出版社2009
[135]李红梅．中国商业银行整体风险管理研究[D]．博士学位论文，辽宁大学，2010．
[136]李平，马婷婷．基于Copula的我国商业银行整体风险度量[J]．统计与决策，2009（22）：140-142
[137]梁世栋，李勇等．信用风险模型比较分析[J]．中国竹理科学，2002，10（1）：17-22
[138]林静．基于MCMC的贝叶斯生存分析理论及其在可靠性评估中的应用
[D]．博士学位论文，南京理工大学，2008．
[139]刘国成．基于序列Monte Carlo方法的非线性滤波技术研究[D]．博士学位论文，华中科技大学，2009．
[140]刘家鹏．信用组合风险的蒙特卡罗模拟研究[D]．博士，天津大学，2008．
[141]刘小莉．信用风险与市场风险的相关性的度量研究[J]．世界经济情况，2006（7）：17-20
[142]马跃渊，徐勇勇，郭秀娥．MCMC收敛性诊断的方差比法及其应用．[J]．中国卫生统计．2004（1）
[143]茆诗松等．高等数理统计[M]．北京：高等教育出版社，2006
[144]潘海涛．时间序列在股指波动性建模中的应用[D]．硕士学位论文，西安电子科技大学，2009．
[145]庞素琳，王燕鸣．多层感知器信用评价模型研究闭．中山大学学报（自然科学版），2003（7）：21-28
[146]钱正培，贺学强．公司信用风险研究的贝叶斯方法[D]．博士学位论文，中国人民大学，中国人民大学，2010.
[147]全登华．利用极值理论计量银行操作风险[J]．统计与决策，2002（3）．
[148]茹诗松，王静龙，淮晓龙．高等数理统计〔M〕北京：高等教育出版社，2004．
[149]茹诗松．贝叶斯统计学[M]北京：中国统计出版社，1999
[150]石晓军，陈殿左．债券结构、波动率与信用风险—对中国上市公司的实证研究[J]．则经研究，2004（9）：24-32
[151]谭德俊，吕宝华，基于贝叶斯方法的信用风险损失分布研究[[J]．统计与信息论坛，2008（11）：63-66
[152]田玲，蔡秋杰．中国商业银行操作风险度量模型的选择与应用[J]．中国软科学，2003（8）：38-42
[153]童光荣等．计量经济学实验教程[M]．武昌：武汉大学出版社，2008.
[154]托马斯．S．Y．霍，李尚斌．金融建模[M]上海：上海财经大学出版社，2007
[155]汪金菊．贝叶斯模型及其在混沌序列分析中的应用研究[D]．博士学位论文，合肥工业大学，2008
[156]王春峰，康莉．基于遗传规划方法的商业银行信用风险评估模型[J]．系统工程理论与实践，2001（02）：73-79
[157]王春峰，李汉华．小样本数据信用风险评估研究[[J]．管理科学学报，2001（2）：46-51
[158]王春峰，万海晖，张维．商业银行信用风险评估及其实证研究[[J]．管理科学学报，1998（1）：68-72．
[159]王汉斌，樊利娜,杨鑫．贝叶斯下的市场风险资产损失计量[J]．商业研究，2011（3）．
[160]王黎明．变点统计分析的研究进展[J]．统计研究．2003（1）．
[161]王新宇金融市场风险的测度方法与实证研究[M]北京：经济管理出版社2008
[162]吴长凤．ARCH模型及其应用研究[D]．博士学位论文，中国科学院数学与系统科学研究所，2000．
[163]武剑．经济资本配置与操作风险管理[J]．海南金融，2007（3）：1-6
[164]肖振宇．金融混业经营及其风险管理研究[D]．博士学位论文，天津大学，2004
[165]谢云山，信用风险和利率风险的相关性研究[[J]．国际金融研究，2004（10）：52-60
[166]杨一锋．改进贝叶斯算法的商业银行信用风险评估模型[J]．重庆工商大学学报（自然科学版）2010（6）
[167]杨星，张义强．中国上市公司信用风险竹理实证研究一一EDF模型在信用评估中的应用．中国软科学，2004（1）：43-47
[168]于立勇，詹捷辉，金建国．内部评级法中违约概率与违约损失率的测算研究[[J]．统计研究，2004（12）：22-26
[169]张尧庭．贝叶斯统计推断[M]．北京：科学出版社，1991．
[170]张尧庭译．计量经济学贝叶斯推断引论[M]．上海：上海财经大学出版社，2005
[171]张运鹏．基于GARCH模型的金融市场风险的风险研究[D]．博士学位论文，吉林大学，2009
[172]赵丽琴．基于Copula函数的金融风险度量研究[D]．博士学位论文,厦门大学，2009．
[173]郑进城，朱慧明．基于MCMC方法的贝叶斯模型分析[[J]．统计与决策，2005（2）．
[174]钟波，汪青松．基于Bayes估计的金融风险值—VaR计算[[J]，数理统计与管理，2007（3）
[175]周效东，汤书昆．金融风险新领域：操作风险度量与竹理研究[[J]．中国软科学，2003（12）：38-42．
[176]周艳菊,彭俊,王宗润．基于Bayesian_Copula方法的商业银行操作风险度量[J]．中国管理科学，2011（8）
[177]朱崇军．MCMC样本确定的后验密度的收敛性[J]．数学杂志．2002（1）
[178]朱慧明，韩玉启，吴正刚，多重线性回归模型的贝叶斯预测分析[[J]．运筹与管理，2005．
[179]朱慧明，韩玉启．贝叶斯多元统计推断理论[M]．科学出版社，2006：112-120
[180]朱慧明，韩玉启．正态一Wishart先验分布下多重线性回归模型的贝叶斯估计
[[J]．南京理工大学学报（自然科学版）．2002（3）
[181]朱慧明，刘智伟．时间序列向量自回归模型的贝叶斯推断理论[[J]．统计与决策，2004（3）
[182]朱慧明．现代经济管理中的线性贝叶斯推断理论与多总体贝叶斯分类识别方法研究[J]，2003（2）
[183]朱慧明等．贝叶斯计量经济模型[M]．北京：科学出版社，2009
②转移核的定义见茆诗松《高等数理统计》高等教育出版社2006年第2版442页
①桂文林数量经济技术经济研究2010第一期，107-118.
①茆诗松贝叶斯统计中国统计出版社2008第20页朱慧明贝叶斯统计计量经济模型科学出版社2009
①周艳菊．基于Bayesian-Copula方法的商业银行操作风险度量[C]．中国管理科学．2011．17-25
①王灵芝．中国证券市场流动性风险的量化与管理研究．2010．
①林静．基于MCMC的贝叶斯生存分析理论及其在可靠性评估中的应用[C]经济与管理科学籍．2008．
②王茹梦雪中国股票市场风险问题及化解思路[C]经济研究参考2012第45期55-63。
[1] A.D. Gro，F.C. Harmantzis, GJ. Kaple. Operational Risk and Reference Data:Exploring Costs, Capital Requirements and Risk Mitigation. November,2005.URL:http://papers.ssrn.com/so13.
[2] A.J. McNeil. Sampling nested Archimedean copulas[J]. Journal of StatisticalComputation and Simulation,2008,78(6):567.
[3] Acerbi,C.,Tasche D.,Expected Shortfall:a natural coherent alternative to Value atRisk[J].Economics Notes,2002,31(2),379-388.
[4] Ahman, E.I. and D.L. Kao. Rating Drift of High Yield Bonds[J]. Journal ofFixedIncome,1992,3:15-20.
[5] Albert, J. Bayesian Computation with R[M]. New York: Springer,2007.
[6] Altman.E.I, Haldeman R G&Narayanan.ZETA Analysis:A New Model to IdentifyBankruptcyRisk of Corporations[J].Journal of Banking&Finance,1977(1),29-54.
[7] Altman.E.I,Financial Ratios,Discriminate Analysis and the Prediction of CorporateBankruptcy [J].Journal of Finance1968,23(9),589-609.
[8] Altman.E.L,Credit Risk Measurement:Developments Over the last20Years[J].Journal of Banking And Finance1997(21).
[9] Ariane Chapelle Yves Crama Georges Hubner, Jean-Philippe Peters. Measuring andManaging Operational Risk in the Financial Sector: An Integrated Framework.February,2005.URL: http://papers.ssrn.com/so13.
[10] Ashby A J, Leon R V, Thyagarajan J. Bayesian Modeling of Accelerated Life Testswith Random Effects[J]. http://stat.bus.utk.edu/techrpts/2003/2003-Ol.pdf.
[11] Barclay G. Techniques of Population Analysis[M]. New York: Wiley.1958.
[12] Basel Committee on Banning Supervision, InternationalMeasurement andCapital Standards, A Revised Framework[R].http://www.bis.org/publ/bcbs118. pdfConvergence of Capital Updated November,2005.
[13] Basel II: International Convergence of Capital Measurement and Capital Standards:aRevised Framework[R],Basel Committee Publications, June2004. http://papers.ssrn. com/so13.
[14] Bauwens L., Lubrano M.. Bayesian inference on GARCH models using the Gibbssampler[J].The Econometrics Journal,1998(1).
[15] Bayes T. An essay towards solving a problem in the doctrine of chances[J].Reprinted in Biometrilca，1958(2).
[16] Beare, B. Copula-based mixing conditions for Markov chains[D], mimeo，University of Oxford,2007.
[17] Berger J O，贾乃光译.统计决策理论和贝叶斯分析[M]，北京：中国统计出版社，1998.
[18] Berger J. O.. Bayesian Analysis: A Look at Today and Thoughts of Tomorrow[J].Journal of The American Statistical Association,2000,95(30), pp:1269-1276.
[19] Berger, J.O. Statistical Decision Theory and Bayesian Analysis[M]. New York:Springer-Verlag,1985.
[20] Berger, J., Ghosh, J.K., Mulchopadhyay, N. Approximations to the Bayes Factor inModel Selection Problems and Consistency Issues[J]. Journal of Statistical Planningand Inference,2003(112):241-258.
[21] Box G E P, Tiao G C. Bayesian inference in statistical analysis[M]. NewYork: JohnWiley and Sons，1992.
[22] Bulter J W. Machine sampling from give probabolity distrbutions.In:Symposium onmonte Carlo Methods[M].edited by Meyer M A, New york:Wiley,1958.
[23] Chiara Cornalba, Paolo Giudici.Statistical models for operational risk management
[M]. Sica A.2004,338:166-172.
[24] Chris Maten.Managing bank capital Capital Allocation and PerformanceMeasurement[M]. Second edition Wiley,2000.
[25] Christian Locher, Jens I. Mehlau, Oliver Wild. Towards risk adjusted controlling ofstrategic IS projects in banks in the light of Basel11[M].annual Hawaii internationalconference on system sciences，2004.
[26] Congdon P Applied Bayesian Modelling[M].England: John Wiley and Sons.2003.
[27] Congdon P Bayesian Statistical Modelling[M].England: John Wiley and Sons，2001.
[28] Corradi V.and.R.Swanson. Predictive Density Evaluation, in Handbook ofEconomic Forecasting,G.Elliott,C.W.J.Granger and A.Timmermann[J].ed.s, NorthHolland, msterdam.2005.
[29] Cox D R, Oakes D. Analysis of Survival Data.[M]. New York: Chapman andHa11.1984.
[30] Credit Suisse. Credit Risk+: A Credit Risk Management Framework [R]. CreditSuisse Financial Products,1997
[31] Credit Suisse.Credit Risk+: A Credit Risk Management Framework[R].Credit SuisseFinancial Products,1997.http://u.csfb.com/institutional/research/assets/creditrisk.pdf.
[32] CreditMetrics[R].Technical Document, JPMorgan，1997.
[33] Crosbie, P.J. and J.R. Bohn Modeling default risk[D] KMV working paper，2002.
[34] Dempster A P,Laird N,Rubin D B.Maximam likelihood estimation from incompletedata via the EM algorithm (with discussion).J Roy Statist1977.
[35] Duffie D. and K. Singleton Modeling term structures of default able bonds.CreditRisk:Princing, Measurement,and Management. Princeton University PressEricRosenberg,2003.
[36] Embrechts, P., Furrer, H., Kaufmann, R., Quantifying regulatory capital foroperational risk, Derivatives Use [J]. Trading&Re gulation.2003(9):217-233.
[37] Fisher, W.D.Estimation in the Linear Decision Model[J].International EconomicRcview,1962(3)：1-29.
[38] Fotios C. Harmantzis. Operational Risk Management in Financial Services and theNe+Basel Accord. URL[D]: http://papers.ssrn.com/so13
[39] Fotios C. Harmantzis. Operational Risk Management in Financial Services and theNew Basel Accord[D].URL: http://papers.ssrn.com/so13.
[40] Frachot A，Georges P. and Roncalli T.Loss distribution approach for operational risk,Group de Recherche Operationnelle[R], Credit Agricole, dance www. gloriamundi.org,2001.
[41] Frachot A.Georges erational riskGroup www.gloriamundi.org,Pde and R.oncalli TLoss distribution Recherche Operationnelle[M], Credit approach Agricole,forop-France，2001.
[42] Frachot, A，Roncalli, T. and Salomon, E.The correlation problem in op-erational risk.Groupde Recherche Operationnelle[D], Credit Agricole, dance, http://www.gloriamundi.org/picsresources/aftres.pdf,2004.
[43] Frances P. H., Hoke H., Pap R. Bayesian analysis of seasonal unit root and meansshifts[J].Journal of Econometrics,1997(3), pp:359-380.
[44] Gelfand A E,Smith A F M. Gibbs sampling for marginal posterior expections[J].Communications in Statistics A,1991.
[45] Gelfand A E,Smith A F M.Sampling based approaches to calculating marginaldensities [J].J Amer Statist Assoc,1990.
[46] Gelman A, Carlin J B, Stern H S, Rubin D B. Bayesian Data Analysis[M].NewYork：Chapman andHaIUCRC，2004.
[47] Gelman A,Carlin J B,Sten H S,Bubin D B.Bayesian data analysis[J].New York：Chapman Hall,1995.
[48] Gilks W R, Richardson S. Spiegelhalter D J. Markov Chains Monte Carlo inpractice.Chapman and Hall,1996.
[49] Gunther Helbok, Christian Wagner. Determinants of Operational Risk Reporting inthe Banking Industry[D]. December23,2005. URL: http://papers.ssrn.com/so13
[50] Hasting W K. Monte Carlo sampling methods using Markov Chains and theirapplications[M].Biometrika,1970.
[51] Hayakawa丫Irony T, Min X. System and Bayesian Reliability[M]. Singapore:World Scientific Publishing.2001
[52] Hildreth, C.Bayesian Statisicians and Remote Clients[J] Econometrics,1963（31）：422-438.
[53] Hoek H., Lucas A., Van Dijk H. K.. Classical and Bayesian aspects of robust unitroot inference[J]. Journal of Econometrics,1995,69(1), pp:27-59.
[54] Hosmer D, Lemeshow S. Applied Survival Analysis[M]. New York: Wiley,1999.
[55] Hougaard P.Analysis of Multivariate Survival Data[M]. New York: Springer-Verlag,2000.
[56] Ilkka K. Methods and problems of software reliability estimation[J]. URL: http://www.vtt.fi/publications/index.jsp.Journal of Derivatives,1995,3(2):73-84.293-15
[57] Jouanin, J.F Rapuch, G, Riboulet, G.&Roncalli, T. Modelling Dependence forCredit[S],2001.
[58] JPMorgan. RiskMetrics Technical Document[M].3rd.New York: JPMorgan.1996.
[59] K. Aas, C.and et Czado. Pair-copula constructions of multiple dependence[J].Mathematics and Economics,2007,02(001):23-24.
[60] Kadam,Migration.A，. Bayesian Inference for Issuer Heterogeneity in Credit RatingJournal of Banking and Finance[J], Lenk,2008(32): P2267-2274.
[61] Kadam,Migration.A., Lenk, P. Bayesian Inference for Issuer Heterogeneity in CreditRating[J].Journal of Banking and Finance,2008(32):2267-2274.
[62] Kasuya M, Tanemura T. Small scale Bayesian VAR modeling logistic diffusion.Journal of Forecasting,2002,20(2):246-253.
[63] KMV Corporation.Credit Monitor Overview [R].SanFrancisco California,1993.
[64] Lawless J F. Statistical Models and Method for Lifetime Data[M]. New York: JohnWiley&Sons,1982.
[65] Li, D.X.(2000) On default correlation: a copula function approach[J]. FixedIncome,943-54.
[66] Lindley D V "Approximate Bayesian methods" in Bayesian Statistics[MJ. Valencia,Spain: Valencia Press.1980
[67] Litterman R B. Forecasting with Bayesian vector auto-regressions: Five years ofexperience [J]. Journal of Business and Economic Statisties.1986,4(1), pp:5-15.
[68] Lubrano M.. Testing for unit roots in a Bayesian framework[J]. Journal ofEconomtrics,1995,69{1), pp:81-109.
[69] Makov U E, Smith A F M, Liu Y H. Bayesian Methods in Actuarial Science[J].'heStatistician.1996
[70] Martz H F, Waller R A. Bayesian reliability analysis[M]. New York: JohnWiley&Son.1982
[71] Masson.PaulContagion:MonsoonalEffects,Spillovers,and Jumps between MultipleEquilibria,IMF(working paper)1998,I42.
[72] McNeil A. Embrechts, P., D. Straumann. Correlation pitfalls and alternatives[J].Risk London Risk Magazine Limited,1999,12:69-71.
[73] McNeil, A, Wendin, J. Dependent Credit Migrations[J]. Journal of Credit Risk,2006,(2):87-114.
[74] McNeil, A., Wendin, J. Bayesian Inference for Generalized Linear Mixed Models ofPortfolio Credit Risk[J]. Journal of Empirical Finance,2007,(l4):131-149.
[75] Monahan J. F.. Fully Bayesian analysis of ARMA timeEconometries, Serves models[J]. Journal of1983,21(2), pp:307-331.
[76] Mori T., Harada E. Internal Risk Based Approach-Evolutionary Approaches toRegulatory Capital Charge for Operational Risk [Z]. Working Paper. Bank of Japan.2000.
[77] P Artzner, F. Delbaen. Default Risk insurance and incomplete markets[J].Mathematical Finance,1995,5(3):187-195.
[78] Pai J.Bayesian Analysis of Compound Loss Distributions[J]. Journal ofEconometrics.1997(3).
[79] Peter B, McCullouch R,Rossi P. Account-level Modeling for Promotion, AnApplication of a constrained Parameter Hierarchical Model[J]. Journal oftheAmerican StatisticaAssociation.1999,94:1063-1073
[80] Phillips P. C. B.Bayes models and forecasts of Australian macroeconomic timeseries[J]. Cowles Foundation Paper987,1994, pp:53-86.
[81] Pickands J. Statistical inference using extreme order statistics[J].Annals ofStatistics,1975(3),119-131.
[82] Press S J. Bayesian Statistics, Principle, models and applications[M]. New York:Wiley.1991
[83] Qin, D. Bayesian Econometrics The First Twenty Years[J]. Econometric Theory,1996(12):500-516.
[84] Quantitive methods in credit management: A survey. Operation Eric Bouye, V D.,Ashkan Nikeghbali,Gael Riboulet,Thierry Roncalli, Copulas:an open field forrisk management. Electronic copy,2001.available at: http://ssrn. com/abstract=1032557,2-8[J]. Survey,July-August,1994
[85] R. Litterman, T. Iben. bond valuation and the term structure of credit spreads[J].Corporate Journal of Portfolio Management,1991(Spring).
[86] Rangan Gupta. Forecasting the South African economy with VARs and VECMs [J].South African Journal of Economics,2006(74), pp:611-628.
[87] Robinson D G. A Hierarchical Bayes Approach to System Reliability Analysis[R].Sandia National Laboratories.USA.2001
[88] Rosenberg, J. V., Schuermann,T., A general approach to integrated risk managementwith skewed, fat-tailed risks[J].Journal of Banking and Finance,2006,(8),2517-2534.
[89] Rosenberg, J.V, Schuermann,T.A general approach to integrated risk managementwith skewed, fat-tailed risks[J].Journal of Financial Economics,2006,79(3):569-614
[90] Rubinstein R Y.Simulation and Monte Carlo methods[M]. New York:John Wiley-Sons,1981.
[91] Sandor, Zsolt, Michel WedeL. Designing Conjoint Choice Experiments UsingManagers' Prior Beliefs[J]. Journal of Marketing Research,2001(28):430-444.
[92] Schotman P C, Van Dijk H. K. A Bayesian analysis of the unit root hypothesis in realexchange rates[J]. Journal of Econometrics,1991(6), pp:195-238.
[93] Sehotman P. C, Van Dijk H. K. On Bayesian routes to unit roots[J]. Journal ofApplied Econometrics,1991(6), pp:387-401.
[94] Sims C. A. Bayesian skepticism on unit root econometrics[J]. Journal of EconomiesDynamics and Control,1988(4), pp:463-474.
[95] Sims C. A., T. Z Bayesian methods for dynamic multivariate models[J]. InternationalEconomic Review,1998,39(4), pp:949-968.
[96] Sinha D, Ibrahim1q Chen M H. Bayesian models for survival data from cancerprevention studies[R].Department of Mathematical Sciences, Worcester PolytechnicInstitute.2000.
[97] Sklar. Fonctions derartition a n dimensions etleurs marges[J]. Statistics UnivParis1959,8:229-231.
[98] Spiegelhalter, D.J., Best, N.G., Carlin, B.P., Van der Linde. A Bayesian Measures ofModel Complexity and Fit[J]. Journal of the Royal Statistical Society Series B,2002,64(3):583-639
[99] Spiegelhalter, D.J.et al. WinBUGS Version1.4User Manual[M]. MRCBiostatistics Unit, Cambridge,2003.
[100] Stefanescu, C., Tunaru, R., Turnbull, S. The Credit Rating Process and Estimationof Transition Probabilities A Bayesian Approach[J]. Journal of Empirical Finance,2009(16):216-234.
[101] Therneau T, Grambsch P. Modeling Survival Data[M]. New York: Springer,2000
[102] Uhlig H. Bayesian vector auto-regressions with stochastic volatility[M].Econometrica,1997.
[103] Vicente N A,Jesus O. Alternative regression models for censored data from the PHmodel to themodifiedsemiparametricAFTmodel[J]. http://www.wiwi.uni-bielefeld.dehkauermann/survivaVnunez-anton.pdf65(1), pp:59-73.
[104] Wald A，王福宝译.统计决策函数[M].上海：上海教育出版社，1963.
[105] West M., Harrison J. Bayesian forecasting and dynamic models[J].2ndedition.NewYork: Springer,1997.
[106] Wilson.T.Portfolio credit risk[R]. Risk,1997,10(9).
[107] Wu C F J.On the convergence properties of the EM algorithm[J].in:The Annals ofStatistics,198.
[108] Ze11ner A，Min C.Gibbs sampler convergence criteria [J]. Journal of Americanstatistical association,1995(2), pp:763-778.
[109] Zellner A. Bayesian estimation and Prediction using asymmetric loss functions [J].Journal ofAmerican Statistical Association,1986,81(2), pp:446-451.
[110] Zellner A. On the Bayesian estimation of multivariate regression [J]. Journal ofroyal statistical society B,1964,26(2), pp:277-285.
[111] Zellner A., Chong C. Forecasting international growth rates using Bayesianshrinkage and other Procedures[J]. Journal of Econometries,1989,40(2), pp:183-202.
[112] Zellner, A. and Tiao, G. C.Bayesian Analysis of the Region Modei withAutocarelated Errors [J].1. of the American Statistical Association.1964(59),pp.763-778.
[113][美]克里斯．莫里森金融风险度量概论[M]北京：清华大学出版社2008.
[114] Kotzs，吴喜之．现代贝叶斯统计学[M]．北京：中国统计出版社，2000
[115]毕文杰．基于粗糙集与贝叶斯理论的不确定信息群决策方法研究[D]．博士学位论文，中南大学，2008.
[116]薄纯林，王宗军．基于贝叶斯网络的商业银行操作风险管理[J]．金融理论与实践，2008（4）
[117]陈珂．基于Bayes估计与极值理论的VaR研究[D]．硕士学位论文，重庆大学，2009．
[118]陈公越，于盟．金融风险测量和全面风险管理[M]上海：上海科学技术出版社，2011
[119]陈学华，杨辉耀，黄向阳．POT模型在商业银行操作风险度量中的应用[J]．管理科学，2003（1）
[120]程功等．信息噪音、结构化模型与银行违约概率度量[[J]．管理科学学报，2007（4）：38-48
[121]程建，连玉君，刘奋军．信用风险模型的贝叶斯改进研究[J]．国际金融研究，2009（1）：63-68
[122]丁东洋．信用风险分析中贝叶斯方法及其应用研究[D]．博士学位论文，天津财经大学，2009．
[123]董磊磊．基于贝叶斯网络的突发事件链建模研究[D]．硕士学位论文，大连理工，2009．
[124]樊欣，杨晓光．中国商业银行也操作风险分析[R]． CFEF研究报告，RR/03/09,2003
[125]方洪全，曾勇．多标准等级判别模型在银行信用风险评估中的应用研究[J]．金融研究2004（9）：92-100
[126]冯静，刘琦，周经纶等．相关函数融合法及其在可靠性分析中的应用．[J]．系统工程与电子技术．2003（2）
[127]高丽君，李建平，徐伟宣，王书平．基于POT方法的商业银行操作风险极端值估计[J]．运筹与管理，2007（1）：1-6
[128]高丽君，李建平等．操作风险度量模型与方法研究[[J]．管理评论．Vo1．18No．9（2006）：8-18．125
[129]郭百钢．基于Bayes网络的投资项目评估与决策方法研究[D]南京理工大学2004．
[130]侯成琪，王频．基于连接函数的整合风险度量研究[J]．统计研究，2008,25（11）72-80
[131]胡素华．连续时间资产收益模型的贝叶斯分析[D]．博士学位论文，天津大学，2007．
[132]金浩．经济统计分析与SAS应用[M]．北京：经济科学出版社2002.
[133]孔丽娜． ARCH模型族的贝叶斯分析及其在经济中的应用[D]．博士学位论文，暨南大学．中国全文数据库经济与管理科学辑，2009（9）
[134]黎子良，邢海鹏，金融市场中的统计模型和方法[M]北京：高等教育出版社2009
[135]李红梅．中国商业银行整体风险管理研究[D]．博士学位论文，辽宁大学，2010．
[136]李平，马婷婷．基于Copula的我国商业银行整体风险度量[J]．统计与决策，2009（22）：140-142
[137]梁世栋，李勇等．信用风险模型比较分析[J]．中国竹理科学，2002，10（1）：17-22
[138]林静．基于MCMC的贝叶斯生存分析理论及其在可靠性评估中的应用
[D]．博士学位论文，南京理工大学，2008．
[139]刘国成．基于序列Monte Carlo方法的非线性滤波技术研究[D]．博士学位论文，华中科技大学，2009．
[140]刘家鹏．信用组合风险的蒙特卡罗模拟研究[D]．博士，天津大学，2008．
[141]刘小莉．信用风险与市场风险的相关性的度量研究[J]．世界经济情况，2006（7）：17-20
[142]马跃渊，徐勇勇，郭秀娥．MCMC收敛性诊断的方差比法及其应用．[J]．中国卫生统计．2004（1）
[143]茆诗松等．高等数理统计[M]．北京：高等教育出版社，2006
[144]潘海涛．时间序列在股指波动性建模中的应用[D]．硕士学位论文，西安电子科技大学，2009．
[145]庞素琳，王燕鸣．多层感知器信用评价模型研究闭．中山大学学报（自然科学版），2003（7）：21-28
[146]钱正培，贺学强．公司信用风险研究的贝叶斯方法[D]．博士学位论文，中国人民大学，中国人民大学，2010.
[147]全登华．利用极值理论计量银行操作风险[J]．统计与决策，2002（3）．
[148]茹诗松，王静龙，淮晓龙．高等数理统计〔M〕北京：高等教育出版社，2004．
[149]茹诗松．贝叶斯统计学[M]北京：中国统计出版社，1999
[150]石晓军，陈殿左．债券结构、波动率与信用风险—对中国上市公司的实证研究[J]．则经研究，2004（9）：24-32
[151]谭德俊，吕宝华，基于贝叶斯方法的信用风险损失分布研究[[J]．统计与信息论坛，2008（11）：63-66
[152]田玲，蔡秋杰．中国商业银行操作风险度量模型的选择与应用[J]．中国软科学，2003（8）：38-42
[153]童光荣等．计量经济学实验教程[M]．武昌：武汉大学出版社，2008.
[154]托马斯．S．Y．霍，李尚斌．金融建模[M]上海：上海财经大学出版社，2007
[155]汪金菊．贝叶斯模型及其在混沌序列分析中的应用研究[D]．博士学位论文，合肥工业大学，2008
[156]王春峰，康莉．基于遗传规划方法的商业银行信用风险评估模型[J]．系统工程理论与实践，2001（02）：73-79
[157]王春峰，李汉华．小样本数据信用风险评估研究[[J]．管理科学学报，2001（2）：46-51
[158]王春峰，万海晖，张维．商业银行信用风险评估及其实证研究[[J]．管理科学学报，1998（1）：68-72．
[159]王汉斌，樊利娜,杨鑫．贝叶斯下的市场风险资产损失计量[J]．商业研究，2011（3）．
[160]王黎明．变点统计分析的研究进展[J]．统计研究．2003（1）．
[161]王新宇金融市场风险的测度方法与实证研究[M]北京：经济管理出版社2008
[162]吴长凤．ARCH模型及其应用研究[D]．博士学位论文，中国科学院数学与系统科学研究所，2000．
[163]武剑．经济资本配置与操作风险管理[J]．海南金融，2007（3）：1-6
[164]肖振宇．金融混业经营及其风险管理研究[D]．博士学位论文，天津大学，2004
[165]谢云山，信用风险和利率风险的相关性研究[[J]．国际金融研究，2004（10）：52-60
[166]杨一锋．改进贝叶斯算法的商业银行信用风险评估模型[J]．重庆工商大学学报（自然科学版）2010（6）
[167]杨星，张义强．中国上市公司信用风险竹理实证研究一一EDF模型在信用评估中的应用．中国软科学，2004（1）：43-47
[168]于立勇，詹捷辉，金建国．内部评级法中违约概率与违约损失率的测算研究[[J]．统计研究，2004（12）：22-26
[169]张尧庭．贝叶斯统计推断[M]．北京：科学出版社，1991．
[170]张尧庭译．计量经济学贝叶斯推断引论[M]．上海：上海财经大学出版社，2005
[171]张运鹏．基于GARCH模型的金融市场风险的风险研究[D]．博士学位论文，吉林大学，2009
[172]赵丽琴．基于Copula函数的金融风险度量研究[D]．博士学位论文,厦门大学，2009．
[173]郑进城，朱慧明．基于MCMC方法的贝叶斯模型分析[[J]．统计与决策，2005（2）．
[174]钟波，汪青松．基于Bayes估计的金融风险值—VaR计算[[J]，数理统计与管理，2007（3）
[175]周效东，汤书昆．金融风险新领域：操作风险度量与竹理研究[[J]．中国软科学，2003（12）：38-42．
[176]周艳菊,彭俊,王宗润．基于Bayesian_Copula方法的商业银行操作风险度量[J]．中国管理科学，2011（8）
[177]朱崇军．MCMC样本确定的后验密度的收敛性[J]．数学杂志．2002（1）
[178]朱慧明，韩玉启，吴正刚，多重线性回归模型的贝叶斯预测分析[[J]．运筹与管理，2005．
[179]朱慧明，韩玉启．贝叶斯多元统计推断理论[M]．科学出版社，2006：112-120
[180]朱慧明，韩玉启．正态一Wishart先验分布下多重线性回归模型的贝叶斯估计
[[J]．南京理工大学学报（自然科学版）．2002（3）
[181]朱慧明，刘智伟．时间序列向量自回归模型的贝叶斯推断理论[[J]．统计与决策，2004（3）
[182]朱慧明．现代经济管理中的线性贝叶斯推断理论与多总体贝叶斯分类识别方法研究[J]，2003（2）
[183]朱慧明等．贝叶斯计量经济模型[M]．北京：科学出版社，2009