证券投资基金市场风险度量研究
|
摘要
在金融全球化和自由化的背景下，随着证券投资基金市场的发展，基金的市场风险越来越成为金融实务界、学术界和监管机构共同关注的对象。作为现代风险管理理论的核心与基础，VaR风险价值理论及在其基础上发展形成的CVaR条件风险价值方法已成为风险测度研究的前沿课题。本篇论文是围绕着基金的市场风险测度展开讨论的，研究的领域涉及到基金收益率时间序列模型的各个方面。特别是针对基金收益率时间序列的尖峰厚尾特征与异方差现象，在风险价值VaR与条件风险价值CVaR的研究中，先后引入了GARCH模型与Copula连接函数进行探讨。试图在前人工作的基础上给出自己的研究思路，并拓展一些理论分析和实证检验方法。
本文的第一章为绪论。主要阐述了本文的研究背景及选题意义,研究的基本思路与主要内容,综述了国内外关于VaR风险价值量、CVaR条件风险价值与Copula连接函数的研究现状。并简要介绍了本文的基本框架。
本文的第二章为证券投资基金的传统市场风险度量方法评析。本章系统的研究了基金的传统市场风险度量方法,主要包括名义量法、均值方差分析、灵敏度方法、波动性方法。对基金市场风险度量方法的发展历程进行了简要回顾，对传统的各种风险度量方法进行了点评。对各方法的优缺点进行了分析。
本文的第三章对VaR的风险度量方法及在其基础上改进产生的CVaR方法方法进行了详细介绍与实证研究。
第四章讨论并建立了基金收益率的波动模型，详细介绍了基金收益率的波动模型——GARCH模型族，以及GARCH模型建模程序，并结合上证指数收益率进行了实证分析。
第五章为基于GARCH模型的VaR计算方法及应用，分别讨论了VaR计算模型、VaR计算的基本模块、传统的VaR计算方法、基于GARCH模型的VaR计算方法与计算步骤，分别构建GARCH-N分布模型、GARCH-t分布模型和GARCH-GED分布模型，进行样本基金的VaR值计算和实证分析；
第六章为基于GARCH模型的CVaR风险度量，介绍了CVaR模型的计算方法、基于GARCH模型的CVaR计算设计、基于CVaR的基金风险度量实证研究。本章用GARCH族模型进行基金市场的波动性模拟,分别在传统正态分布假设、刻画尖峰重尾特性的学生t分布及GED分布假设下,结合不同基金公司的9只三种类型的开放式基金来进行实证研究。
本文第七章详细介绍了基于Copula函数的GARCH模型优化方法——Copula-GARCH，并利用其进行沪深股市的日收益率相关性分析，同时尝试基于蒙特卡洛模拟算法进行基金投资组合的Clayton Copula-VaR实证计算。。
本文第八章为全文总结，并对论文的不足和未来的研究方向进行了展望。
In the background of the financial globalization and liberalization, with thedevelopment of market of securities investment funds, Market risks management is ahot topic in financial institutions, academia, and financial supervisors. As the coreand Foundation of the modern theory of risk management, theory of VaR and itsformation based on CVaR method has become the current topics of research on riskmeasurement. This essay is discussions focus on the Fund market risk measures,research areas related to yield time-series model, the varios aspects of the Fund. Inparticular, against the income of the Fund rate time series of Spike characteristics ofheavy-tailed and heteroscedasticity, VaR and CVaR study, has introduced the GARCHmodel to explore connected with the Copula function. Attempting to predecessors onthe basis of the work to their own research, and expand theoretical analysis andempirical methods.
The first chapter is the introduction of this article. It mainly elaborates theresearch on the research topic selection of backgrounds and significance of this article,basic ideas and main contents of the study, a summary of the domestic value of VaRrisk and CVaR research status of conditional value at risk connected with the Copulafunction. And it outlines the basic framework of this article.
Chapter II of this article is about the traditional market risk measurement methodof securities investment funds. Systems in this chapter on the Fund's traditionalmarket risk measurement methods, mainly by including name, mean-VaRianceanalysis, sensitivity, volatility methods. To fund development of market riskmeasurement method for a brief review, the traditions of the various comments on therisk measurement methods. Analysis of the advantages and disadvantages of eachmethod.
This third chapter is about risk measurement method of VaR and itsimprovement on the basis of CVaR method.
The fourth chapter discussed and established a fund yield fluctuation model,details the funds rate fluctuation model--GARCH model and GARCH model modeling programs, and combined with yield on the index of empirical analyses.
Fifth is based on calculation of VaR method and its application of GARCHmodel, discusses the calculation of VaR models, VaR calculation of basic modules,traditional methods of calculation of VaR, VaR calculation method and algorithmbased on GARCH models, respectively, the construction of GARCH-N distributionmodel, GARCH-t model and GARCH-GED model, sample VaR value calculationand analysis of the Fund;
Chapter VI is on CVaR risk measurement based on GARCH models, introducedthe CVaR calculation method, based on the model of CVaR computational design of aGARCH model and the empirical study based on CVaR risk measurement. Thischapter fund market volatility with GARCH model simulation, respectively, in thetraditional normal distribution assumptions, depicting spike characteristics ofheavy-tailed Student's t distribution and distribution assumptions of GED, with9different fund companies to conduct empirical research on three types of open-endfunds.
Chapter VII is about Copula methods of empirical research on investmentportfolio risk measurement. This chapter at Southern high growth Fund’s top holdingsconstitutes the investment portfolio as a sample, using the Copula function to studythe risks of the investment portfolio, calculate the VaR of the portfolio and CVaR.
The last chapte is the full-text summary and the research direction in the futureprospects.
本文的第一章为绪论。主要阐述了本文的研究背景及选题意义,研究的基本思路与主要内容,综述了国内外关于VaR风险价值量、CVaR条件风险价值与Copula连接函数的研究现状。并简要介绍了本文的基本框架。
本文的第二章为证券投资基金的传统市场风险度量方法评析。本章系统的研究了基金的传统市场风险度量方法,主要包括名义量法、均值方差分析、灵敏度方法、波动性方法。对基金市场风险度量方法的发展历程进行了简要回顾，对传统的各种风险度量方法进行了点评。对各方法的优缺点进行了分析。
本文的第三章对VaR的风险度量方法及在其基础上改进产生的CVaR方法方法进行了详细介绍与实证研究。
第四章讨论并建立了基金收益率的波动模型，详细介绍了基金收益率的波动模型——GARCH模型族，以及GARCH模型建模程序，并结合上证指数收益率进行了实证分析。
第五章为基于GARCH模型的VaR计算方法及应用，分别讨论了VaR计算模型、VaR计算的基本模块、传统的VaR计算方法、基于GARCH模型的VaR计算方法与计算步骤，分别构建GARCH-N分布模型、GARCH-t分布模型和GARCH-GED分布模型，进行样本基金的VaR值计算和实证分析；
第六章为基于GARCH模型的CVaR风险度量，介绍了CVaR模型的计算方法、基于GARCH模型的CVaR计算设计、基于CVaR的基金风险度量实证研究。本章用GARCH族模型进行基金市场的波动性模拟,分别在传统正态分布假设、刻画尖峰重尾特性的学生t分布及GED分布假设下,结合不同基金公司的9只三种类型的开放式基金来进行实证研究。
本文第七章详细介绍了基于Copula函数的GARCH模型优化方法——Copula-GARCH，并利用其进行沪深股市的日收益率相关性分析，同时尝试基于蒙特卡洛模拟算法进行基金投资组合的Clayton Copula-VaR实证计算。。
本文第八章为全文总结，并对论文的不足和未来的研究方向进行了展望。
In the background of the financial globalization and liberalization, with thedevelopment of market of securities investment funds, Market risks management is ahot topic in financial institutions, academia, and financial supervisors. As the coreand Foundation of the modern theory of risk management, theory of VaR and itsformation based on CVaR method has become the current topics of research on riskmeasurement. This essay is discussions focus on the Fund market risk measures,research areas related to yield time-series model, the varios aspects of the Fund. Inparticular, against the income of the Fund rate time series of Spike characteristics ofheavy-tailed and heteroscedasticity, VaR and CVaR study, has introduced the GARCHmodel to explore connected with the Copula function. Attempting to predecessors onthe basis of the work to their own research, and expand theoretical analysis andempirical methods.
The first chapter is the introduction of this article. It mainly elaborates theresearch on the research topic selection of backgrounds and significance of this article,basic ideas and main contents of the study, a summary of the domestic value of VaRrisk and CVaR research status of conditional value at risk connected with the Copulafunction. And it outlines the basic framework of this article.
Chapter II of this article is about the traditional market risk measurement methodof securities investment funds. Systems in this chapter on the Fund's traditionalmarket risk measurement methods, mainly by including name, mean-VaRianceanalysis, sensitivity, volatility methods. To fund development of market riskmeasurement method for a brief review, the traditions of the various comments on therisk measurement methods. Analysis of the advantages and disadvantages of eachmethod.
This third chapter is about risk measurement method of VaR and itsimprovement on the basis of CVaR method.
The fourth chapter discussed and established a fund yield fluctuation model,details the funds rate fluctuation model--GARCH model and GARCH model modeling programs, and combined with yield on the index of empirical analyses.
Fifth is based on calculation of VaR method and its application of GARCHmodel, discusses the calculation of VaR models, VaR calculation of basic modules,traditional methods of calculation of VaR, VaR calculation method and algorithmbased on GARCH models, respectively, the construction of GARCH-N distributionmodel, GARCH-t model and GARCH-GED model, sample VaR value calculationand analysis of the Fund;
Chapter VI is on CVaR risk measurement based on GARCH models, introducedthe CVaR calculation method, based on the model of CVaR computational design of aGARCH model and the empirical study based on CVaR risk measurement. Thischapter fund market volatility with GARCH model simulation, respectively, in thetraditional normal distribution assumptions, depicting spike characteristics ofheavy-tailed Student's t distribution and distribution assumptions of GED, with9different fund companies to conduct empirical research on three types of open-endfunds.
Chapter VII is about Copula methods of empirical research on investmentportfolio risk measurement. This chapter at Southern high growth Fund’s top holdingsconstitutes the investment portfolio as a sample, using the Copula function to studythe risks of the investment portfolio, calculate the VaR of the portfolio and CVaR.
The last chapte is the full-text summary and the research direction in the futureprospects.
引文
[1] Aragon J.R, Blanco.Carlos，Dowd.Kevin．Incorporating Stress Tests into Market RiskModeling［Ｊ］.Derivatives Quarterly.2001. Spring：44-49
[2] Castillo and Hade．1997. Fitting the Generalized Pareto Distribution to Date［Ｊ］.Journal ofthe American Statistical Association.92(440):1609-1620.
[3] Chen, M.-Y． Application of Quintile Regression to Estimation of Value at Risk, manuscript,Department of Economics. Taiwan：National Chung Cheng University,2002.
[4] Kristofferson． Evaluating Interval Forecasts［Ｊ］.International Economic Review，l998，39(4)：841-864.
[5] Danielson and devise．1997. Beyond the Sample: Extreme Quintile and ProbabilityEstimation［D］. LSE and Tinbergen Institute.
[6] Davison and Smith． Models for Exceedances over High Thresholds［Ｊ］.Journal of RoyalStatistical Society，1990：B52,393-442.
[7] Duffle and Pan． An Overview of Value at Risk［Ｊ］.Journal of Derivatives,1997：47-49.
[8] Dupuis．1998. Exceedances over Nigh Thresholds: A Guide to Threshold Selection.Extrmes.1(3):251-261.
[9] Embrocates, Kloppenburg and Mikosch． Modeling Extremal Events for Insurance andFinance［Ｊ］. Springer Verlag, New York，1997.
[10] Grimshaw． Computing the Maximum Likelihood Estimates for the Generalized ParetoDistribution to Data［Ｊ］.Technometrics，1993.35(2):185-191.
[11] Hull and White． Value at Risk When Daily Changes in Market VaRiables Are not NormallyDistributed［Ｊ］.Journal of Derrivatives,1998：5.9-19
[12] Irina and Svetlozar．Value at Risk: Recent Advances［D］.University of California, SantaBarbara and University of Karlsruhe, Germany.
[13] J.P.Morgan．Risk Metrics-Technology Document [M].4th ed. New York.1996.
[14] Kluppelberg Claidia．Risk Management With Extreme Value Theory［M］.University ofMunich.2002.March.
[15] Dowd, Kevin．The Extreme Value Approach to VaR-AnIntroduction［M］.FinancialEngineering News，August.
[16] Philippe Jorion．Risk：Measuring the Risk in Value at Risk［Ｊ］.Financial Analysts Journal，1996:47-56
[17] Rachel Campbell，Ronald Huisman，Kees Koedijk．Optimal portfolio selection in aValue-at-Risk framework［Ｊ］.Journal of Banking&Finance，2001(25)：1789-1804
[18] Rich. Don．Second Generation VaR and Risk-Adjusted Return on Capital［Ｊ］.Journal ofDerivatives，2003. Summer：51-61
[19] Alexander CO．Orthogonal GARCH In Mastering Ris［kM］.FT-Prentice Hall:London,2001,2:21–38
[20] Annalisa Clemente, Claudio Romano．A Copula-Extreme Value Theory Approach forModeling Operational Risk［M］.2003(3):117-156
[21] Alexandra Dias, Paul Embrechts．The ART of dependence modelling-the latest advances incorrelation analysis［Ｊ］.The European Journal of Finance,2004(6):23-34
[22] B.Efron．Bootstrap Methods:Another Look at the Jackknife［Ｊ］.Annals of Statistics,1979,7:1-26
[23] Bollerslev T．Generalized autoregressive conditional heteroscedasticity［Ｊ］.Journal ofEconometrics,1986,31:307–327
[24] J.S. Butler, Barry Schechter． Improving value-at-risk estimates by combining kernelestimation［Ｊ］.Proceedings, Federal Reserve Bank of Chicago,1996:363-380
[25] Chris Brooks, Gita Persand．The Effect of Asymmetries on stocl index return value at riskestimates［Ｊ］.Journal of Risk Finance,2003,21:119—131
[26] Embrechts P，Lindskog F, McNeil A．Modeling dependence with copulas andapplications torisk management.［M］.2001:145-156
[27] Embrechts P. McNeil, Straumann． Correlation:pitfalls and alternatives ［Ｊ］.Riskmagazine,1999(5)：69-71
[28] Engle RF．Autoregressive conditional heteroscedasticity with estimates of the VaRiance ofUnited Kingdom inflation［Ｊ］.Econometrica1982,50:987-1007
[29] Engle RF．Dynamic conditional correlation:a simple class of multi Variate generalizedautoregressive conditional heteroskedasticity models［Ｊ］.Journal of Businessand Economic Statistics2002,20:339-350
[30] Jean-David, Fremaniana．Olivier. Sensitivity analysis of VaR and Expected Shortfall forportfolios under netting agreements［Ｊ］.Journal of Banking&Finance,2005.29(4):45-57
[31] Jobson J D, Korkie．Putting Markowitz to work［Ｊ］.Journal of Portfolio Management,1981,7(4):70-74
[32] Joe.H．MultiVaRiate Models and Dependence Concept［sＪ］.Chapman&Hall, London,1997:23-45
[33] John C Hull．Risk Management and Financial Institutions［Ｊ］.Pearson Education，2007:93-99
[34] Kupiec PH． Technipues for Verifying the Accuracy of Risk Measurement Models
［Ｊ］.Journal of Derivatives,1995,2:73-84
[35] McAleer M． Automated inference and learning in modeling financial volatility
［Ｊ］.Econometric Theory,2005,21:232–261
[36] Michael McALEER．Bernardo Da Veiga. ForeCasting Value-at-Risk Parsimonious PortfolioSpillover GARCH(PS-GRACH) Model［Ｊ］.Journal of Forecasting,2008,27:1-19
[37] Nelsen R.B．An Introduction to Copulas，New York: Lectores Notosin Statisties［Ｊ］.SpringVerlag,1998:124-139
[38] Nelsen R.B．An Introduction to Copulas，Lecture Notes in Statistic［M］.Springer-Verlag,NewYork,1999:321-334
[39] Risk Metrics．Risk Metrics Technical Documen［tＪ］.J.P. Morgan,3rd ad,1996(8):15-37[26]Skylar, A. Functions de Repartition a Dimensions et Leurs Marges, Publ. Inst. Statist.
[40]陈忠阳．金融风险分析与管理研究［M］.中国人民大学出版社，2001.4
[41]菲利普乔瑞．VaR风险价值——金融风险管理新标准［M］.张海渔译，中信出版社2000年10月版。
[42]寇日明，陈雨露，孙永红译.风险管理实务［M］.中国金融出版社，2000年2月版。
[43]皮埃特罗潘泽，维普K班塞尔．用VaR度量市场风险［M］.綦相译，机械工业出版社，2001:9.
[44]王春峰．金融市场风险管理［M］.天津大学出版社，2001年2月版。
[45]王卫东．现代银行全面风险管理［Ｊ］.中国经济出版社，2001:10
[46]阎春宁．风险管理学［Ｊ］.上海大学出版社，2002:7
[47]张尧庭．金融市场的统计分析［Ｊ］.广西师范大学出版社，1998
[48]章彰．商业银行信用风险管理一一兼论巴塞尔新资本协议［Ｊ］.中国人民大学出版社，2002:11.
[49]杜海涛．VaR模型在证券风险管理中的应用［Ｊ］.证券市场导报，2000:8.
[50]戴国强、徐龙炳．VaR方法对我国金融风险管理的借鉴及应用［Ｊ］.金融研究，2000：7
[51]范英．VaR方法及其在股市风险分析中的应用初探［Ｊ］.中国管理科学，2000：3.
[52]黄海．投资银行的风险管理和VaR技术的应用［Ｊ］.财经研究，1998：8.
[53]黄智猛、曹钧华．VaR风险监管下金融机构的决策模型与分析［Ｊ］.系统工程理论方法应用，1999：4.
[54]李友华、王吉恒．VaR模型在我国金融风险管理中的运用研究［Ｊ］.农村金融研究，2000：2.
[55]刘静．VaR应用分析以及对我国的启示［Ｊ］.南开经济研究》，2002：3,36—37
[56]刘宇飞．VaR模型及其在金融监管中的应用［Ｊ］.经济科学，1999：1，39-49.
[57]龙海明、黄卫．VaR在消费信贷风险管理中的应用.财经理论与实践［Ｊ］.2002：6，31-36.
[58]牛昂．银行风险管理的新方法，国际金融研究，1997：7.
[59]彭文德．金融资产奢侈风险的VaR计量方法及其应用［Ｊ］.金融研究，1999:11
[60]王春峰、万海辉．基于MCMC的金融市场风险VaR的估计［Ｊ］.管理科学学报，2000：6.
[61]吴世农、陈斌．风险度量方法与金融资产配置模型的理论和实证研究［Ｊ］.经济研究，1999：9.
[62]宋锦智．VaR值的三种估算方法及其比较［Ｊ］.城市金融论坛，2000：12.
[63]叶青．基于GARCH和半参数法的VaR模型及其在中国股市风险分析中的应用［Ｊ］.统计研究，2000：12.
[64]于研．风险值法在金融机构信用风险管理中的运用［Ｊ］.统计研究，2003：7，49—53.
[65]姚刚．风险值测定方法浅析［Ｊ］.经济科学，1998:1.
[66]郑明川、徐翠萍．衍生金融工具风险信息的VaR披露模式［Ｊ］.会计研究，2002:7，49—53.
[67]郑文通．金融风险管理的VaR方法及其应用［Ｊ］.国际金融研究，1997：9，58—62.
[68]詹原瑞．市场风险的量度——VaR的计算与应用［Ｊ］.系统工程理论与实践，1999：12.
[69]李静，王伟．VaR模型在我国证券投资基金风险管理中的应用［J］，理论月刊，2005-1
[70]周屏等．基于开放式基金的市场风险度量，(硕士学位论文)，湖南大学，2007-05
[71]张敏．基于VaR-GARCH模型的我国开放式基金风险实证研究［J］，内蒙古农业大学学报，2007-03
[72]陈玲玲．基于VaR的开放式基金市场风险度量及业绩评价实证研究，(硕士学位论文)，浙江理工大学，2007：11
[73]陈权宝，连娟．对我国开放式基金风险的实证研究［Ｊ］.经济问题，2008：09
[74]徐跃等．基金大趋势——战略投资开放式基金［M］.上海财经大学出版社，2002
[75]蒲明．我国开放式基金风险管理［Ｊ］.黑龙江对外经贸，2008：05
[76]陈小红．开放性基金的VaR测算与评估［Ｊ］.武汉金融，2006：08
[77]刘春章，黄桐城，陈汉军．风险调整收益及其在投资决策中的应用［Ｊ］.决策借鉴,2002,
[78]马国顺．一种新的风险度量指标［Ｊ］.西北师范大学学报(自然科学版),1999：(2),19-21
[79]邹辉文,陈德棉．关于风险的若干问题及其在风险投资中的应用［Ｊ］.同济大学学报,2002：(9),1145-1151
[80]陈金龙，张维．金融资产的市场风险度量模型及其应用［Ｊ］.华侨大学学报(哲学社会科学版),2002：(3),29-36
[81]张宏业．证券组合风险的分析［Ｊ］.中央财经大学学报,2000：(6),46-48
[82]曹永刚，王萍，类成曜．现代金融风险［M］.北京：中国金融出版社,2000
[83]傅志超．股票投资风险的度量与控制［Ｊ］.经济数学,1994：(1),60-63
[84]朱世武，张尧庭，徐小庆．一种新的股市风险度量指标及其应用［Ｊ］.经济数学,2002：(6),1-9
[85]刘一凡，宋福铁．基于VaR方法对开放式基金风险评估的实证研究［Ｊ］.华东理工大学学报（社会科学版）,2006(2):43-56
[86]潘红宇．时间序列分析[M].北京:对外经济贸易大学出版社,2006:65-79
[87]韦艳华,张世英．金融市场的相关性分析——Copula-GARCH模型及其应用［Ｊ］.系统工程,2004(4):7-12
[88]韦艳华,张世英．金融市场非对称尾部相关结构的研究［Ｊ］.管理学报,2005,2(5):
[89]韦艳华，张世英．Copula理论及其在金融分析上的应用[M].北京:清华大学出版社,2008:14-68
[90]杨湘豫，高楠楠．中国开放式基金投资组合风险值的实证——基于Copula-GARCH的分析［Ｊ］.财经理论与实践,2008,7:53-57
[2] Castillo and Hade．1997. Fitting the Generalized Pareto Distribution to Date［Ｊ］.Journal ofthe American Statistical Association.92(440):1609-1620.
[3] Chen, M.-Y． Application of Quintile Regression to Estimation of Value at Risk, manuscript,Department of Economics. Taiwan：National Chung Cheng University,2002.
[4] Kristofferson． Evaluating Interval Forecasts［Ｊ］.International Economic Review，l998，39(4)：841-864.
[5] Danielson and devise．1997. Beyond the Sample: Extreme Quintile and ProbabilityEstimation［D］. LSE and Tinbergen Institute.
[6] Davison and Smith． Models for Exceedances over High Thresholds［Ｊ］.Journal of RoyalStatistical Society，1990：B52,393-442.
[7] Duffle and Pan． An Overview of Value at Risk［Ｊ］.Journal of Derivatives,1997：47-49.
[8] Dupuis．1998. Exceedances over Nigh Thresholds: A Guide to Threshold Selection.Extrmes.1(3):251-261.
[9] Embrocates, Kloppenburg and Mikosch． Modeling Extremal Events for Insurance andFinance［Ｊ］. Springer Verlag, New York，1997.
[10] Grimshaw． Computing the Maximum Likelihood Estimates for the Generalized ParetoDistribution to Data［Ｊ］.Technometrics，1993.35(2):185-191.
[11] Hull and White． Value at Risk When Daily Changes in Market VaRiables Are not NormallyDistributed［Ｊ］.Journal of Derrivatives,1998：5.9-19
[12] Irina and Svetlozar．Value at Risk: Recent Advances［D］.University of California, SantaBarbara and University of Karlsruhe, Germany.
[13] J.P.Morgan．Risk Metrics-Technology Document [M].4th ed. New York.1996.
[14] Kluppelberg Claidia．Risk Management With Extreme Value Theory［M］.University ofMunich.2002.March.
[15] Dowd, Kevin．The Extreme Value Approach to VaR-AnIntroduction［M］.FinancialEngineering News，August.
[16] Philippe Jorion．Risk：Measuring the Risk in Value at Risk［Ｊ］.Financial Analysts Journal，1996:47-56
[17] Rachel Campbell，Ronald Huisman，Kees Koedijk．Optimal portfolio selection in aValue-at-Risk framework［Ｊ］.Journal of Banking&Finance，2001(25)：1789-1804
[18] Rich. Don．Second Generation VaR and Risk-Adjusted Return on Capital［Ｊ］.Journal ofDerivatives，2003. Summer：51-61
[19] Alexander CO．Orthogonal GARCH In Mastering Ris［kM］.FT-Prentice Hall:London,2001,2:21–38
[20] Annalisa Clemente, Claudio Romano．A Copula-Extreme Value Theory Approach forModeling Operational Risk［M］.2003(3):117-156
[21] Alexandra Dias, Paul Embrechts．The ART of dependence modelling-the latest advances incorrelation analysis［Ｊ］.The European Journal of Finance,2004(6):23-34
[22] B.Efron．Bootstrap Methods:Another Look at the Jackknife［Ｊ］.Annals of Statistics,1979,7:1-26
[23] Bollerslev T．Generalized autoregressive conditional heteroscedasticity［Ｊ］.Journal ofEconometrics,1986,31:307–327
[24] J.S. Butler, Barry Schechter． Improving value-at-risk estimates by combining kernelestimation［Ｊ］.Proceedings, Federal Reserve Bank of Chicago,1996:363-380
[25] Chris Brooks, Gita Persand．The Effect of Asymmetries on stocl index return value at riskestimates［Ｊ］.Journal of Risk Finance,2003,21:119—131
[26] Embrechts P，Lindskog F, McNeil A．Modeling dependence with copulas andapplications torisk management.［M］.2001:145-156
[27] Embrechts P. McNeil, Straumann． Correlation:pitfalls and alternatives ［Ｊ］.Riskmagazine,1999(5)：69-71
[28] Engle RF．Autoregressive conditional heteroscedasticity with estimates of the VaRiance ofUnited Kingdom inflation［Ｊ］.Econometrica1982,50:987-1007
[29] Engle RF．Dynamic conditional correlation:a simple class of multi Variate generalizedautoregressive conditional heteroskedasticity models［Ｊ］.Journal of Businessand Economic Statistics2002,20:339-350
[30] Jean-David, Fremaniana．Olivier. Sensitivity analysis of VaR and Expected Shortfall forportfolios under netting agreements［Ｊ］.Journal of Banking&Finance,2005.29(4):45-57
[31] Jobson J D, Korkie．Putting Markowitz to work［Ｊ］.Journal of Portfolio Management,1981,7(4):70-74
[32] Joe.H．MultiVaRiate Models and Dependence Concept［sＪ］.Chapman&Hall, London,1997:23-45
[33] John C Hull．Risk Management and Financial Institutions［Ｊ］.Pearson Education，2007:93-99
[34] Kupiec PH． Technipues for Verifying the Accuracy of Risk Measurement Models
［Ｊ］.Journal of Derivatives,1995,2:73-84
[35] McAleer M． Automated inference and learning in modeling financial volatility
［Ｊ］.Econometric Theory,2005,21:232–261
[36] Michael McALEER．Bernardo Da Veiga. ForeCasting Value-at-Risk Parsimonious PortfolioSpillover GARCH(PS-GRACH) Model［Ｊ］.Journal of Forecasting,2008,27:1-19
[37] Nelsen R.B．An Introduction to Copulas，New York: Lectores Notosin Statisties［Ｊ］.SpringVerlag,1998:124-139
[38] Nelsen R.B．An Introduction to Copulas，Lecture Notes in Statistic［M］.Springer-Verlag,NewYork,1999:321-334
[39] Risk Metrics．Risk Metrics Technical Documen［tＪ］.J.P. Morgan,3rd ad,1996(8):15-37[26]Skylar, A. Functions de Repartition a Dimensions et Leurs Marges, Publ. Inst. Statist.
[40]陈忠阳．金融风险分析与管理研究［M］.中国人民大学出版社，2001.4
[41]菲利普乔瑞．VaR风险价值——金融风险管理新标准［M］.张海渔译，中信出版社2000年10月版。
[42]寇日明，陈雨露，孙永红译.风险管理实务［M］.中国金融出版社，2000年2月版。
[43]皮埃特罗潘泽，维普K班塞尔．用VaR度量市场风险［M］.綦相译，机械工业出版社，2001:9.
[44]王春峰．金融市场风险管理［M］.天津大学出版社，2001年2月版。
[45]王卫东．现代银行全面风险管理［Ｊ］.中国经济出版社，2001:10
[46]阎春宁．风险管理学［Ｊ］.上海大学出版社，2002:7
[47]张尧庭．金融市场的统计分析［Ｊ］.广西师范大学出版社，1998
[48]章彰．商业银行信用风险管理一一兼论巴塞尔新资本协议［Ｊ］.中国人民大学出版社，2002:11.
[49]杜海涛．VaR模型在证券风险管理中的应用［Ｊ］.证券市场导报，2000:8.
[50]戴国强、徐龙炳．VaR方法对我国金融风险管理的借鉴及应用［Ｊ］.金融研究，2000：7
[51]范英．VaR方法及其在股市风险分析中的应用初探［Ｊ］.中国管理科学，2000：3.
[52]黄海．投资银行的风险管理和VaR技术的应用［Ｊ］.财经研究，1998：8.
[53]黄智猛、曹钧华．VaR风险监管下金融机构的决策模型与分析［Ｊ］.系统工程理论方法应用，1999：4.
[54]李友华、王吉恒．VaR模型在我国金融风险管理中的运用研究［Ｊ］.农村金融研究，2000：2.
[55]刘静．VaR应用分析以及对我国的启示［Ｊ］.南开经济研究》，2002：3,36—37
[56]刘宇飞．VaR模型及其在金融监管中的应用［Ｊ］.经济科学，1999：1，39-49.
[57]龙海明、黄卫．VaR在消费信贷风险管理中的应用.财经理论与实践［Ｊ］.2002：6，31-36.
[58]牛昂．银行风险管理的新方法，国际金融研究，1997：7.
[59]彭文德．金融资产奢侈风险的VaR计量方法及其应用［Ｊ］.金融研究，1999:11
[60]王春峰、万海辉．基于MCMC的金融市场风险VaR的估计［Ｊ］.管理科学学报，2000：6.
[61]吴世农、陈斌．风险度量方法与金融资产配置模型的理论和实证研究［Ｊ］.经济研究，1999：9.
[62]宋锦智．VaR值的三种估算方法及其比较［Ｊ］.城市金融论坛，2000：12.
[63]叶青．基于GARCH和半参数法的VaR模型及其在中国股市风险分析中的应用［Ｊ］.统计研究，2000：12.
[64]于研．风险值法在金融机构信用风险管理中的运用［Ｊ］.统计研究，2003：7，49—53.
[65]姚刚．风险值测定方法浅析［Ｊ］.经济科学，1998:1.
[66]郑明川、徐翠萍．衍生金融工具风险信息的VaR披露模式［Ｊ］.会计研究，2002:7，49—53.
[67]郑文通．金融风险管理的VaR方法及其应用［Ｊ］.国际金融研究，1997：9，58—62.
[68]詹原瑞．市场风险的量度——VaR的计算与应用［Ｊ］.系统工程理论与实践，1999：12.
[69]李静，王伟．VaR模型在我国证券投资基金风险管理中的应用［J］，理论月刊，2005-1
[70]周屏等．基于开放式基金的市场风险度量，(硕士学位论文)，湖南大学，2007-05
[71]张敏．基于VaR-GARCH模型的我国开放式基金风险实证研究［J］，内蒙古农业大学学报，2007-03
[72]陈玲玲．基于VaR的开放式基金市场风险度量及业绩评价实证研究，(硕士学位论文)，浙江理工大学，2007：11
[73]陈权宝，连娟．对我国开放式基金风险的实证研究［Ｊ］.经济问题，2008：09
[74]徐跃等．基金大趋势——战略投资开放式基金［M］.上海财经大学出版社，2002
[75]蒲明．我国开放式基金风险管理［Ｊ］.黑龙江对外经贸，2008：05
[76]陈小红．开放性基金的VaR测算与评估［Ｊ］.武汉金融，2006：08
[77]刘春章，黄桐城，陈汉军．风险调整收益及其在投资决策中的应用［Ｊ］.决策借鉴,2002,
[78]马国顺．一种新的风险度量指标［Ｊ］.西北师范大学学报(自然科学版),1999：(2),19-21
[79]邹辉文,陈德棉．关于风险的若干问题及其在风险投资中的应用［Ｊ］.同济大学学报,2002：(9),1145-1151
[80]陈金龙，张维．金融资产的市场风险度量模型及其应用［Ｊ］.华侨大学学报(哲学社会科学版),2002：(3),29-36
[81]张宏业．证券组合风险的分析［Ｊ］.中央财经大学学报,2000：(6),46-48
[82]曹永刚，王萍，类成曜．现代金融风险［M］.北京：中国金融出版社,2000
[83]傅志超．股票投资风险的度量与控制［Ｊ］.经济数学,1994：(1),60-63
[84]朱世武，张尧庭，徐小庆．一种新的股市风险度量指标及其应用［Ｊ］.经济数学,2002：(6),1-9
[85]刘一凡，宋福铁．基于VaR方法对开放式基金风险评估的实证研究［Ｊ］.华东理工大学学报（社会科学版）,2006(2):43-56
[86]潘红宇．时间序列分析[M].北京:对外经济贸易大学出版社,2006:65-79
[87]韦艳华,张世英．金融市场的相关性分析——Copula-GARCH模型及其应用［Ｊ］.系统工程,2004(4):7-12
[88]韦艳华,张世英．金融市场非对称尾部相关结构的研究［Ｊ］.管理学报,2005,2(5):
[89]韦艳华，张世英．Copula理论及其在金融分析上的应用[M].北京:清华大学出版社,2008:14-68
[90]杨湘豫，高楠楠．中国开放式基金投资组合风险值的实证——基于Copula-GARCH的分析［Ｊ］.财经理论与实践,2008,7:53-57
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |