国债融资风险模拟、测度与预警研究
摘要
财政政策是政府宏观经济调控的重要手段之一,在现代经济社会中具有举足轻重的地位。国债融资是财政收入的重要来源,在弥补财政赤字、筹集建设资金、保障民生需求等方面起到关键作用。政府国债融资决策是否科学、及时、有效直接影响融资成本的高低、融资风险的大小、国家财政金融体系的安全,甚至会引发局部乃至全球性的债务危机。
如何权衡国债融资利息支付成本和成本变动风险之间的关系,特别是识别和测度国债融资成本变动风险,已经成为当前国债融资决策的难点和核心问题。风险识别、模拟与测度是本研究解决当前国债融资难点和核心问题的新思路和新方法,为此创新性工作有：(1)利用数量方法构建国债融资风险环境。市场风险方面,通过模拟方法模拟宏观经济状态变化,计算经济扩张和收缩概率；利率风险方面,通过随机过程模型揭示利率变化的数量特征,模拟国债融资的利率期限结构动态轨迹：(2)通过计算机模拟技术对影响政府国债融资成本的风险因素进行动态化模拟,在模拟样本下建立国债融资最优期限组合决策模型,求解政府可承受风险下融资成本最小化的融资方案；(3)应用非参数分布估计方法估计各融资方案的成本分布,并基于非参数分布估计提出二分算法测度未知分布风险事件发生概率,通过该方法定量测算各融资方案的成本风险特征,以此进行比较评估和压力测试,最终实现成本风险框架下政府国债期限组合的最优决策。
如果将成本风险框架下的政府国债期限组合决策看作国债融资的中间环节,那么何时发行国债和何时偿还国债则是国债融资有始有终的关键问题。本研究对政府国债融资动机、经济功能和作用机制进行剖析探讨,从经济学角度创新性提出国债发行时机的经济学判断标准和国债偿还的成本收益评价方法。
国债融资完成后,如何防范国债违约风险、提前化解债务危机是国债管理中必须考虑的重要问题。本研究提出将风险概率应用到国债风险预警中的新思路,首次通过测度风险指标超越警戒线的概率定量监测并识别国债违约风险。通过自行编程开发将风险概率预警方法转化为风险警示灯系统,通过设置灯号颜色直观表现国债总体风险和各项风险。
本研究利用数量模型、经济学方法、计算机编程、统计概率测算和非参数估计方法等,分别在国债发行时间、期限组合、偿还时间、国债预警等方面进行了方法创新研究,突出了国债研究的多学科、定量化、可实现的研究特色,为我国政府制定国债发行策略和偿还策略、优化国债融资决策、进行国债风险管理,甚至是建立国债政策实验室提供了方法性依据和可行性参考。
Goverment debt, as the tool of government's economic management, plays an important role in modern economic activities. Debt is an important source of government income, which makes up fiscal deficits, raise funds for construction and ensure people's needs. It connects with the financing costs, risks and national finance security whether government financing is scientific, timely and reasonable. The key point of government financing decision-making is to reduce the cost of interest payments, control cost risk and prevent the debt crisis. The difficulty in debt financing is that government can not have both low costs and low risks. The portfolios with lower interest costs are often faced with large uncertainty. The portfolios with small risk often have high interest payments. How can the government make a trade-off between cost and risk, to set up scientific and reasonable financing portfolios, is a significant problem to be solved.
Risk simulaton and risk measures are new ideas to deal with the trade-off between costs and risk. The reason why risk problems are boring is we usally cannot identify sources of risk and measure the risk. To resolve the problem, this study use mathematical model to make risk environment of government financing, simulate changes of risk factors by computer technology, proposed the numerical method to measure risk probability with non-parametric distribution estimation. First, we design a risk simulation system. Markov Chain Monte Carlo is used to simulate macroeconomic state changes. In this process, we calculate the probability of economic expansion and contraction. Stochastic model is used to simulate dynamic of term structure of interest rate. Second, we propose numerical methods to measure risk of costs and assesement to target portofolios. Based on simulation samples, we set an optimaziton model to look for the best portfolios that minimize costs under risk tolerance of government. Non-parametric statistics is used to estimate the distribution of simulation samples. Then, a new numerical method to measure risk is to be propesed. This technology can establish characteristics of cost-risk under any financing portfolios. It also can be applicated in comparative analysis of the various financing portofolios and stress testing.
Government debt financing, as a public service, has beginnings and ends. If debt portpolios make-desition is the middle part, the issue of debt is beginnings and the payment of debt is ends. With the perspective of economics, we analyzed motivation of government debt issuing, study the economic function and mechanism of government financing, proposed the criteria for judging the issurance time, present a cost-benefit method of judging payment time. Against default risk, this study provides a new idea of early warning of risk probability. We measure the probability that risk indicators exceeded the warning level to monitor debt risk, set warning signals to visually show performance of the overall or part risk of debt.
This study is a new try for government to use mathematics and computer technology to improve debt management. We present the methods of risk simulation, risk measurement, risk warning in government financing make-decision, highlighting multi-disciplinary, quantitative characteristics and operation. Of course, any model or method is only support tool for decision, not replace practical experience and judgments of debt manager. We hope this study can be helpful to establishment of the Chinnese debt policy laboratory in the coming future.
如何权衡国债融资利息支付成本和成本变动风险之间的关系,特别是识别和测度国债融资成本变动风险,已经成为当前国债融资决策的难点和核心问题。风险识别、模拟与测度是本研究解决当前国债融资难点和核心问题的新思路和新方法,为此创新性工作有：(1)利用数量方法构建国债融资风险环境。市场风险方面,通过模拟方法模拟宏观经济状态变化,计算经济扩张和收缩概率；利率风险方面,通过随机过程模型揭示利率变化的数量特征,模拟国债融资的利率期限结构动态轨迹：(2)通过计算机模拟技术对影响政府国债融资成本的风险因素进行动态化模拟,在模拟样本下建立国债融资最优期限组合决策模型,求解政府可承受风险下融资成本最小化的融资方案；(3)应用非参数分布估计方法估计各融资方案的成本分布,并基于非参数分布估计提出二分算法测度未知分布风险事件发生概率,通过该方法定量测算各融资方案的成本风险特征,以此进行比较评估和压力测试,最终实现成本风险框架下政府国债期限组合的最优决策。
如果将成本风险框架下的政府国债期限组合决策看作国债融资的中间环节,那么何时发行国债和何时偿还国债则是国债融资有始有终的关键问题。本研究对政府国债融资动机、经济功能和作用机制进行剖析探讨,从经济学角度创新性提出国债发行时机的经济学判断标准和国债偿还的成本收益评价方法。
国债融资完成后,如何防范国债违约风险、提前化解债务危机是国债管理中必须考虑的重要问题。本研究提出将风险概率应用到国债风险预警中的新思路,首次通过测度风险指标超越警戒线的概率定量监测并识别国债违约风险。通过自行编程开发将风险概率预警方法转化为风险警示灯系统,通过设置灯号颜色直观表现国债总体风险和各项风险。
本研究利用数量模型、经济学方法、计算机编程、统计概率测算和非参数估计方法等,分别在国债发行时间、期限组合、偿还时间、国债预警等方面进行了方法创新研究,突出了国债研究的多学科、定量化、可实现的研究特色,为我国政府制定国债发行策略和偿还策略、优化国债融资决策、进行国债风险管理,甚至是建立国债政策实验室提供了方法性依据和可行性参考。
Goverment debt, as the tool of government's economic management, plays an important role in modern economic activities. Debt is an important source of government income, which makes up fiscal deficits, raise funds for construction and ensure people's needs. It connects with the financing costs, risks and national finance security whether government financing is scientific, timely and reasonable. The key point of government financing decision-making is to reduce the cost of interest payments, control cost risk and prevent the debt crisis. The difficulty in debt financing is that government can not have both low costs and low risks. The portfolios with lower interest costs are often faced with large uncertainty. The portfolios with small risk often have high interest payments. How can the government make a trade-off between cost and risk, to set up scientific and reasonable financing portfolios, is a significant problem to be solved.
Risk simulaton and risk measures are new ideas to deal with the trade-off between costs and risk. The reason why risk problems are boring is we usally cannot identify sources of risk and measure the risk. To resolve the problem, this study use mathematical model to make risk environment of government financing, simulate changes of risk factors by computer technology, proposed the numerical method to measure risk probability with non-parametric distribution estimation. First, we design a risk simulation system. Markov Chain Monte Carlo is used to simulate macroeconomic state changes. In this process, we calculate the probability of economic expansion and contraction. Stochastic model is used to simulate dynamic of term structure of interest rate. Second, we propose numerical methods to measure risk of costs and assesement to target portofolios. Based on simulation samples, we set an optimaziton model to look for the best portfolios that minimize costs under risk tolerance of government. Non-parametric statistics is used to estimate the distribution of simulation samples. Then, a new numerical method to measure risk is to be propesed. This technology can establish characteristics of cost-risk under any financing portfolios. It also can be applicated in comparative analysis of the various financing portofolios and stress testing.
Government debt financing, as a public service, has beginnings and ends. If debt portpolios make-desition is the middle part, the issue of debt is beginnings and the payment of debt is ends. With the perspective of economics, we analyzed motivation of government debt issuing, study the economic function and mechanism of government financing, proposed the criteria for judging the issurance time, present a cost-benefit method of judging payment time. Against default risk, this study provides a new idea of early warning of risk probability. We measure the probability that risk indicators exceeded the warning level to monitor debt risk, set warning signals to visually show performance of the overall or part risk of debt.
This study is a new try for government to use mathematics and computer technology to improve debt management. We present the methods of risk simulation, risk measurement, risk warning in government financing make-decision, highlighting multi-disciplinary, quantitative characteristics and operation. Of course, any model or method is only support tool for decision, not replace practical experience and judgments of debt manager. We hope this study can be helpful to establishment of the Chinnese debt policy laboratory in the coming future.
引文
5 经济周期与预警系统.北京：科学出版社,1990.
6 经济周期波动的分析与预测方法.长春：吉林大学出版社.1998.
7 中国转轨时期的经济周期波动-理论、方法及实证分析.北京：科学出版社,2009.
[1]Abken P. A., An Empirical Evaluation of Value at Risk by Scenario Simulation[J], Journal of Derivatives,2000,7:12-18.
[2]Auditor General of Canada, Managing Canada's Debt:Facing New Challenges[R], Discussion paper, 2000.
[3]Andreas Pick and Myrvin Anthony, A simulation model for the analysis of the UK's sovereign debt strategy[R], The United Kingdom Debt Management Office,2006.
[4]Angoua P., V.S. Lai and I. Soumare, Project risk choices under privately guaranteed debt financing[J], The Quarterly Review of Economics and Finance,2008,1:123-152
[5]Brennan M.J. and E.S. Schwartz, An Equilibrium Model of Bond Pricing and a Test of Market Efficiency[J], Journal of Financial and Quantitative Analysis,1982,17.
[6]Barro, R. J., Optimal Debt Management[R], National Bureau of Economic Research:Working Paper No.5327,1995.
[7]Barro R. J., Optimal Debt Management [R], National Bureau of Economic Research:Working Paper No.5327,1995.
[8]Bergstrom and Holmlund, A Simulation Model Framework for Government Debt Analysis[R], The Swedish National Debt Office,2000.
[9]Black R. and C. Telmer, Liability Management Using Dynamic Portfolio Strategies [J], Algo Research Quarterly,1999,2:9-19.
[10]Bremaud P., Markov Chains:Gibbs Fields, Monte Carlo Simulation and Queues[M], Springer Verlag, 1999.
[11]Brigo D. and F. Mercurio, Interest Rate Models:Theory and Implementation[M], Berlin, Germany: Springer Verlag,2001.
[12]Bolder D.J., Towards a More Complete Debt Strategy Simulation Framework[R], Bank of Canada Working Paper No.2002-13,2002.
[13]Bolder D.J., A Proposed Stochastic Simulation Framework for the Government of Canada's Debt Strategy Problem[R], Bank of Canada, Working Paper No.2003-10,2003.
[14]Bolder D.J., Optimization in a Simulation Setting:Use of Function Approximation in Debt Strategy Analysis[R], Bank of Canada Working Paper No.2007-13,2007.
[15]Campbell and A. C. MacKinlay, The Econometrics of Financial Markets[M], Princeton University Press, Princeton, New Jersey,1997.
[16]Cassard and D. Folkerts-Landau, Risk Management of Sovereign Assets and Liabilities[R], Research Department of International Monetary Fund,1997.
[17]Cox J. C., J. E. Ingersoll, and S. A. Ross, An Intertemporal General Equilibrium Model of Asset Prices[J], Econometrica,1985(a),53:363-384.
[18]Cox J. C., J. E. Ingersoll, and S. A. Ross, A Theory of Term Structure of Interest Rates[J], Econometrica,1985(b),53:385-407.
[19]Dempster and Rubin, Maximum Likelihood from Incomplete Data via the EM Algorithm[J], Journal of the Royal Statistical Society, Series B,1977,39:1-38.
[20]Danish National Bank, Management of Interest Rate and Refinancing Risk:Cost-at-Risk[R], Discussion Paper, Working Party on Debt Management,1998(a).
[21]Danish National Bank, The Interest and Refinancing Risk on Domestic Government Debt-Cost at Risk[R], Discussion paper,1998(b).
[22]DeGrauwe P., International Money:Post-War Trends and Theories[M], Oxford University Press, 1989.
[23]DeJong F., Time-Series and Cross-Section Information in Affine Term Structure Models[J], Journal of Business and Economic Statistics,2000(18):300-314.
[24]Durland J. M. and T. H. McCurdy, Duration-Dependent Transitions in a Markov Model of U.S. GNP Growth[J], Journal of Business and Economic Statistics,1994,12(3):279-288.
[25]Durrett R., Probability:Theory and Examples[M], Duxbury Press, California, second edn,1996.
[26]Filardo A. J., Business Cycle Phases and Their Transitional Dynamics[R], Federal Reserve Bank of Kansas City, Working Paper No.93-14,1993.
[27]Filardo A. J. and S. F. Gordon, Business Cycle Durations[R], Federal Reserve Bank of Kansas City, Working Paper No.93-11,1993.
[28]Filardo A. J., International Co-Movements of Business Cycles[R], Federal Reserve Bank of Kansas City, Working Paper No.94-11,1994.
[29]Filardo A. J., Choosing Information Variables for Transition Probabilities in a Time-Varying Transition Probability Markov Switching Model[R], Federal Reserve Bank of Kansas City, Working Paper No.93-14,1998.
[30]Fishman G. S., Monte Carlo:Concepts, Algorithms and Applications[M], Springer-Verlag,1995.
[31]Gerhard Bry and Charlotte Boschan,Cyclical Ananysis of Time Series:Selected Procedures and Computer Programs[R], New York,Technical P20,1971.
[32]Goldfeld S. M., and R. E. Quandt, A Markov Switching Model for Switching Regressions[J], Journal of Econometrics,1973(1):3-16.
[33]Gray S., The Management of Government Debt[M], Bank of England,1976.
[34]Glasserman P., Monte Carlo Methods in Financial Engineering[M], Springer,2003.
[35]Hamilton J. D., A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle[J], Econometrica,1989,57:357-384.
[36]Hamilton J. D., Analysis of Time Series Subject to Changes in Regime[J], Journal of Econometrics, 1990,45:39-70.
[37]Hamilton J. D., Time Series Analysis[M], Princeton University Press, Princeton,1994.
[38]Hamilton J. D., Specification Testing in Markov-Switching Time Series Models[J], Journal of Econometrics,1996,70:127-157.
[39]Heath D., R. Jarrow and A. Morton, Bond Pricing and The Term Structure of the Interest Rates:A New Methodology[J], Econometrica,1992,60:77-105.
[40]Hermanutz D. and C. Matier, Modelling Federal Finances under Uncertainty[R], Department of Finance Canada,2001.
[41]Horngren L., Methods for Analyzing the Structure of the Central Government Debt[R], The Swedish National Debt Office,1999.
[42]Hostland D., Specification of a Stochastic Simulation Model for the Analysis of Monetary and Fiscal Policy[R], Department of Finance Canada,2001(b).
[43]Hostland D. and C. Matier, An Examination of Alternative Strategies for Reducing Public Debt in the Presence of Uncertainty[R], Department of Finance Canada,2001.
[44]Huu Tue Huynh, Van Son Lai and Issouf Soumare, Stochasitic Simulation and Applications in Finance with MATLAB Programs[M], John Wiley&Sons Ltd, England,2008.
[45]Ingves S. and J. Goldstein, Draft Guidelines for Public Debt Management[R], International Monetary Fund and The World Bank,2000.
[46]IMF and the World Bank, Guidelines for Public Debt Management:Accompanying Document and Selected Case Study[M],2001.
[47]Jakel P., Monte Carlo Methods in Finance[M], John Wiley&Sons Ltd,2002.
[48]Karatzas I. and S. E. Shreve, Brownian Motion and Stochastic Calculus[J], Springer-Verlag, Berlin, second edn,1991.
[49]Kim C.-J., Dynamic Linear Models with Markov-Switching[J], Journal of Econometrics,1994,60: 1-22.
[50]Kim C.-J. and C. R. Nelson, State-Space Models with Regime Switching[M], The MIT Press, Cambridge, Massachusetts, Chapters 4-5,1999.
[51]Longstaff F. A. and E. S. Schwartz, Interest Rate Volatility and the Term Structure:A Two-Factor General Equilibrium Model[J], The Journal of Finance,1992, XL VII,1259-1282.
[52]Missale A., Public Debt Management[D], PHD Dissertation:Massachusetts Institute of Technology,1994.
[53]Meyn S. P. and R. L. Tweedie, Markov Chains and Stochastic Stability[M], Springer-Verlag, New York,1993.
[54]Morgan Stanley Dean Witter, Debt Strategy Analysis[R], Discussion Paper, Working Paper No.10-Fixed Income Research,1999.
[55]Marrison C., The Fundamental of Risk Measurement[M], McGraw-Hill,2002.
[56]Martinez W.L. and A.R. Martinez, Computational Statistics Handbook with MATLAB[M], Chapman&Hall,2002.
[57]MathWorks, MATLAB7.1, Database Toolbox User's Guide[P],2005.
[58]MathWorks, MATLAB7.1, Fixed-Income Toolbox User's Guide[P],2005.
[59]MathWorks, MATLAB7.1, Financial Toolbox User's Guide[P],2005.
[60]MathWorks, MATLAB7.1, Financial Time Series Toolbox User's Guide[P],2005.
[61]Oksendal B. K., Stochastic Differential Equations[M], Springer Verlag, Berlin, fourth edn,1995.
[62]Pesaran M. H. and S. M. Potter, Nonlinear Dynamics and Econometrics:An Introduction[J], Journal of Applied Econometrics,1992,7:S1-S7.
[63]Paolo Brandimarte, Numerical Method in Finance-A MATLAB Based Introduction[M], John Wiley&Sons Ltd,2004.
[64]Risbjerg L. and A. Holmlund, Analytical Framework for Debt and Risk Management in Advances in Risk Management of Government Debt[R], OECD, Paris, France,2005.
[65]Schaller H. and S. van Norden, Regime Switching in Stock Market Returns[J], Applied Financial Economics,1997,7:177-191.
[66]Swedish Ministry of Finance, Guidelines for Central Government Debt Management: Decision taken at the Cabinet Meeting 8 November 2001 [R], Discussion paper,2001.
[67]Swedish National Debt Office, Central Government Debt Management-Proposed Guidelines[R], Discussion paper,2001.
[68]Swedish National Debt Office, Central Government Borrowing:Forecast and Analysis[R], Discussion paper,2002.
[69]Teukolsky, Vetterling and Flannery, Numerical Recipes:The Art of Scientific Computing[M], Cambridge University Press, Cambridge, second edn,1992.
[70]Takero Doi,Toshihiro Ihori and Kiyoshi Mitsui,Sustainability, Debt Management and Public Debt Policy in Japan[R],Working Paper, NBER, July 2006.
[71]Vasicek O., An Equilibrium Characterization of the Term Structure[J], Journal of Financial Economics,1977,5:177-188.
[72]Wheeler G., New Zealand's Experience with Autonomous Sovereign Debt Management[R], New Zealand Debt Management Office,1996.
[73]刘黎明.国家自然科学基金项目《财政体制的博弈分析》,G030301,2001.
[74]刘黎明.财政部横向课题《国债金融预警系统》,2001.
[75]刘黎明,刘玲玲.国债发行期限结构的最优化模型[C].全国数量经济年会,2004.
[76]刘黎明.财政体制的理论与模型方法研究[M].北京：首都经济贸易大学出版社,2007：47-57.
[77]刘黎明.财政转移支付的博弈分析[M].北京：中国财政经济出版社,2000.
[78J刘玲玲,冯健身.中国公共财政[M].北京：经济科学出版社,1999.
[79]刘玲玲.公共财政学[M].北京：清华大学出版社,2000.
[80]贾颖,刘黎明.当前我国国债风险与规模的统计分析[J].北京工业大学学报(社会科学版),2001,4.
[81]王宁,刘黎明.金融风险预警系统设计及实现[J].北京工业大学学报(社会科学版),2001,2.
[82]杨义群,钟兴文,胡征月.国债筹资成本优化的数学模型[J].数量经济技术经济研究.2000,10：25-27.
[83]周军民,赵旭,杨义群.国债规模与发行成本优化问题的研究[J].财经研究,2000,4：3-7.
f84]胡祖光.国债期限的比较研究[J].数量经济技术经济研究.2002,5：13-16.
[85]李庆峰.国债期限与成本优化关系研究[J].数量经济技术经济研究.2004,10：158-161.
[86]宋永明,昌忠泽.国债期限结构理论及其在成本管理中的应用[J].财经问题研究.2004,2：19-22.
[87]靳俐.成本控制于结构管理—对我国当前国债问题的相关思考[J].财贸研究,2002,6：83-86.
[88]龙安国.论国债期限结构的优化[J].科技情报开发与经济,2003,5：147-148.
[89]李向东,谢智波.我国国债期限结构优化的理论研究[J].经济体制改革,2003,3：104-107.
[90]张妹妹.合理设计国债期限结构的必要性探索[J].现代管理科学,2005,6：118-119.
[91]杨大楷,杨勇.关于我国国债收益率曲线的研究[J].财经研究,1997,7：14-19.
[92]姚长辉,梁跃军.我国国债收益率曲线的实证研究[J].金融研究,1998,8：12-18.
[93]吴硕思.随机期限结构与国债定价模型[J].中国管理科学,1999,1：13-20.
[94]陈雯,陈浪南.国债利率期限结构：建模与实证[J].世界经济,2000,8：24-28.
[95]谢赤,吴雄伟.基于Vasicek模型和CIR模型中的中国货币市场利率行为实证分析[J].中国管理科学,2002,6：22-25.
[96]郑振龙,林海.中国市场利率期限结构的静态估计[J].武汉金融,2003,3：33-37.
[97]林海,郑振龙.中国利率动态模型模型[J].财经问题研究,2005,9：45-49.
[98]刘金全,郑挺国.利率期限结构的马尔科夫区制转移模型与实证分析[J].经济研究,2006,11：82-91.
[99]朱世武,陈健恒.交易所国债利率期限结构实证研究[J].金融研究,2003,10：63-73.
[100]傅曼丽,董荣杰,屠美曾.动态利率模型估计方法的一个实证检验[J].华中科技大学学报(自然科学版),2005,4.
[101]潘冠中,邵斌.单因子利率模型的极大似然估计[J].财经研究,2004,10.
[102]胡志强,萧毅海.债券定价与即期利率、远期利率关系分析[J].数量经济技术经济研究,2005,1.
[103]贾康,赵全厚.国债适度规模与我国国债的现实规模[J].经济研究,2000,10：58-62.
[104]高培勇.国债管理也是一种重要的宏观经济调控手段[J].金融与经济,1993,4：19-20.
[105]高培勇.论举借国债的经济作用机制[J].经济研究,1996,9：24-31.
[106]高培勇.债务依存度：透视当前中国财政困难状况的一面镜子[J].国际经济评论,1997,5：28-30.
[107]高培勇.调整完善长期建设国债政策和深化财政体制改革[J].经济研究参考,2004,1.
[108]高培勇,温来成.中长期国债偿还机制：现状、预测与政策选择[J].国际经济评论,2004,9：26-29.
[109]李扬.中国国债市场的发展和国债管理政策[J].教学与研究,1997,8：17-21.
[110]李扬.国债规模：在财政与金融之间寻求平衡[J].财贸经济,2003,1：51-57.
[111]王宁.关于我国国债管理目标和方式的几点思考[J].财政研究,2008,12：29-31.
[112]哈维·罗森,特德·盖亚著.郭庆旺,赵志耘译.财政学[M].北京：中国人民大学出版社,2009：444-454.
[113]陈时兴.中国转型期国债的金融分析[M].北京：中国社会科学出版社,2001：89-121.
[114]陈时兴,姚明龙.我国国债规模的金融经济分析[J].数量经济技术经济研究,2001,5：12-16.
[115]平新乔.道德风险与政府的或然负债[J],财贸经济,2000,11.
[116]程萍.积极财政政策下的债务风险及其防范[J].财贸研究,2002,2：60-62.
[117]吴海霞,邢春华,孙婵娟.运用信号分析法建立我国的金融风险预警系统[J].金融论坛,2004,6：51-56.
[118]丛树海,李生详.我国财政风险指数预警方法的研究[J].财贸经济,2004,6：29-35.
[119]约翰·赫尔著.张陶伟译.期权、期货和其他衍生产品[M].北京：华夏出版社,2004：188-197.
[120]赵谦.我国国债的风险和成本管理研究[D].哈尔滨工业大学博士论文,2007：3-10.
[121]王亚芬,梁云芳.我国财政风险预警系统的建立与应用研究[J].财政研究,2004,11：25-27.
[122]高铁梅,王金明,陈飞,梁云芳.中国转轨时期的经济周期波动-理论、方法及实证分析[M].2009：6-72.
[123]高鸿业.西方经济学[M].北京：中国人民大学出版社,2000：720-754.
[124]多恩·布什著.李庆云等校译.宏观经济学[M].北京：中国人民大学出版社,1997：491-512.
[125]刘树成.经济周期与预警系统[M].北京：科学出版社,1990.
[126]董文泉.经济周期波动的分析与预测方法[M].长春：吉林大学出版社,1998.
[127]吴喜之.非参数统计[M].北京：中国统计出版社,1999：1-16.
[128]沃塞曼著.吴喜之译.现代非参数统计[M].北京：科学出版社,2008：100-110.
[129]唐林俊,杨虎,张洪阳.核密度估计在预测风险价值中的应用[J].数学的实践与认识,2005,10：29-35.
[130]易丹辉.数据分析与EVIEWS应用[M].北京：中国统计出版社,2002.
[131]张晓峒.计量经济学软件EVIEWS使用指南[M].天津：南开大学出版社,2004.
[132]高铁梅.计量经济分析方法与建模：EVIEWS应用及实例[M].北京：清华大学出版社,2005.
[133]薛毅,陈立萍.统计建模与R软件[M].北京：清华大学出版社,2007：107-200.
[134]汤银才.R语言与统计分析[M].北京：高等教育出版社,2008：89-97.
[135]张树德.MATLAB金融计算与金融数据处理[M].北京：北京航天航空大学出版社,2008.
[136]王浩,刘黎明.国债发行与国债债务终结策略分析的新视角.首都经济贸易大学学报[J].2010,2：35-40.
[137]王浩.美国国债：下一次世界经济危机的隐患[J].中国物价,2010,3：24-25.
6 经济周期波动的分析与预测方法.长春：吉林大学出版社.1998.
7 中国转轨时期的经济周期波动-理论、方法及实证分析.北京：科学出版社,2009.
[1]Abken P. A., An Empirical Evaluation of Value at Risk by Scenario Simulation[J], Journal of Derivatives,2000,7:12-18.
[2]Auditor General of Canada, Managing Canada's Debt:Facing New Challenges[R], Discussion paper, 2000.
[3]Andreas Pick and Myrvin Anthony, A simulation model for the analysis of the UK's sovereign debt strategy[R], The United Kingdom Debt Management Office,2006.
[4]Angoua P., V.S. Lai and I. Soumare, Project risk choices under privately guaranteed debt financing[J], The Quarterly Review of Economics and Finance,2008,1:123-152
[5]Brennan M.J. and E.S. Schwartz, An Equilibrium Model of Bond Pricing and a Test of Market Efficiency[J], Journal of Financial and Quantitative Analysis,1982,17.
[6]Barro, R. J., Optimal Debt Management[R], National Bureau of Economic Research:Working Paper No.5327,1995.
[7]Barro R. J., Optimal Debt Management [R], National Bureau of Economic Research:Working Paper No.5327,1995.
[8]Bergstrom and Holmlund, A Simulation Model Framework for Government Debt Analysis[R], The Swedish National Debt Office,2000.
[9]Black R. and C. Telmer, Liability Management Using Dynamic Portfolio Strategies [J], Algo Research Quarterly,1999,2:9-19.
[10]Bremaud P., Markov Chains:Gibbs Fields, Monte Carlo Simulation and Queues[M], Springer Verlag, 1999.
[11]Brigo D. and F. Mercurio, Interest Rate Models:Theory and Implementation[M], Berlin, Germany: Springer Verlag,2001.
[12]Bolder D.J., Towards a More Complete Debt Strategy Simulation Framework[R], Bank of Canada Working Paper No.2002-13,2002.
[13]Bolder D.J., A Proposed Stochastic Simulation Framework for the Government of Canada's Debt Strategy Problem[R], Bank of Canada, Working Paper No.2003-10,2003.
[14]Bolder D.J., Optimization in a Simulation Setting:Use of Function Approximation in Debt Strategy Analysis[R], Bank of Canada Working Paper No.2007-13,2007.
[15]Campbell and A. C. MacKinlay, The Econometrics of Financial Markets[M], Princeton University Press, Princeton, New Jersey,1997.
[16]Cassard and D. Folkerts-Landau, Risk Management of Sovereign Assets and Liabilities[R], Research Department of International Monetary Fund,1997.
[17]Cox J. C., J. E. Ingersoll, and S. A. Ross, An Intertemporal General Equilibrium Model of Asset Prices[J], Econometrica,1985(a),53:363-384.
[18]Cox J. C., J. E. Ingersoll, and S. A. Ross, A Theory of Term Structure of Interest Rates[J], Econometrica,1985(b),53:385-407.
[19]Dempster and Rubin, Maximum Likelihood from Incomplete Data via the EM Algorithm[J], Journal of the Royal Statistical Society, Series B,1977,39:1-38.
[20]Danish National Bank, Management of Interest Rate and Refinancing Risk:Cost-at-Risk[R], Discussion Paper, Working Party on Debt Management,1998(a).
[21]Danish National Bank, The Interest and Refinancing Risk on Domestic Government Debt-Cost at Risk[R], Discussion paper,1998(b).
[22]DeGrauwe P., International Money:Post-War Trends and Theories[M], Oxford University Press, 1989.
[23]DeJong F., Time-Series and Cross-Section Information in Affine Term Structure Models[J], Journal of Business and Economic Statistics,2000(18):300-314.
[24]Durland J. M. and T. H. McCurdy, Duration-Dependent Transitions in a Markov Model of U.S. GNP Growth[J], Journal of Business and Economic Statistics,1994,12(3):279-288.
[25]Durrett R., Probability:Theory and Examples[M], Duxbury Press, California, second edn,1996.
[26]Filardo A. J., Business Cycle Phases and Their Transitional Dynamics[R], Federal Reserve Bank of Kansas City, Working Paper No.93-14,1993.
[27]Filardo A. J. and S. F. Gordon, Business Cycle Durations[R], Federal Reserve Bank of Kansas City, Working Paper No.93-11,1993.
[28]Filardo A. J., International Co-Movements of Business Cycles[R], Federal Reserve Bank of Kansas City, Working Paper No.94-11,1994.
[29]Filardo A. J., Choosing Information Variables for Transition Probabilities in a Time-Varying Transition Probability Markov Switching Model[R], Federal Reserve Bank of Kansas City, Working Paper No.93-14,1998.
[30]Fishman G. S., Monte Carlo:Concepts, Algorithms and Applications[M], Springer-Verlag,1995.
[31]Gerhard Bry and Charlotte Boschan,Cyclical Ananysis of Time Series:Selected Procedures and Computer Programs[R], New York,Technical P20,1971.
[32]Goldfeld S. M., and R. E. Quandt, A Markov Switching Model for Switching Regressions[J], Journal of Econometrics,1973(1):3-16.
[33]Gray S., The Management of Government Debt[M], Bank of England,1976.
[34]Glasserman P., Monte Carlo Methods in Financial Engineering[M], Springer,2003.
[35]Hamilton J. D., A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle[J], Econometrica,1989,57:357-384.
[36]Hamilton J. D., Analysis of Time Series Subject to Changes in Regime[J], Journal of Econometrics, 1990,45:39-70.
[37]Hamilton J. D., Time Series Analysis[M], Princeton University Press, Princeton,1994.
[38]Hamilton J. D., Specification Testing in Markov-Switching Time Series Models[J], Journal of Econometrics,1996,70:127-157.
[39]Heath D., R. Jarrow and A. Morton, Bond Pricing and The Term Structure of the Interest Rates:A New Methodology[J], Econometrica,1992,60:77-105.
[40]Hermanutz D. and C. Matier, Modelling Federal Finances under Uncertainty[R], Department of Finance Canada,2001.
[41]Horngren L., Methods for Analyzing the Structure of the Central Government Debt[R], The Swedish National Debt Office,1999.
[42]Hostland D., Specification of a Stochastic Simulation Model for the Analysis of Monetary and Fiscal Policy[R], Department of Finance Canada,2001(b).
[43]Hostland D. and C. Matier, An Examination of Alternative Strategies for Reducing Public Debt in the Presence of Uncertainty[R], Department of Finance Canada,2001.
[44]Huu Tue Huynh, Van Son Lai and Issouf Soumare, Stochasitic Simulation and Applications in Finance with MATLAB Programs[M], John Wiley&Sons Ltd, England,2008.
[45]Ingves S. and J. Goldstein, Draft Guidelines for Public Debt Management[R], International Monetary Fund and The World Bank,2000.
[46]IMF and the World Bank, Guidelines for Public Debt Management:Accompanying Document and Selected Case Study[M],2001.
[47]Jakel P., Monte Carlo Methods in Finance[M], John Wiley&Sons Ltd,2002.
[48]Karatzas I. and S. E. Shreve, Brownian Motion and Stochastic Calculus[J], Springer-Verlag, Berlin, second edn,1991.
[49]Kim C.-J., Dynamic Linear Models with Markov-Switching[J], Journal of Econometrics,1994,60: 1-22.
[50]Kim C.-J. and C. R. Nelson, State-Space Models with Regime Switching[M], The MIT Press, Cambridge, Massachusetts, Chapters 4-5,1999.
[51]Longstaff F. A. and E. S. Schwartz, Interest Rate Volatility and the Term Structure:A Two-Factor General Equilibrium Model[J], The Journal of Finance,1992, XL VII,1259-1282.
[52]Missale A., Public Debt Management[D], PHD Dissertation:Massachusetts Institute of Technology,1994.
[53]Meyn S. P. and R. L. Tweedie, Markov Chains and Stochastic Stability[M], Springer-Verlag, New York,1993.
[54]Morgan Stanley Dean Witter, Debt Strategy Analysis[R], Discussion Paper, Working Paper No.10-Fixed Income Research,1999.
[55]Marrison C., The Fundamental of Risk Measurement[M], McGraw-Hill,2002.
[56]Martinez W.L. and A.R. Martinez, Computational Statistics Handbook with MATLAB[M], Chapman&Hall,2002.
[57]MathWorks, MATLAB7.1, Database Toolbox User's Guide[P],2005.
[58]MathWorks, MATLAB7.1, Fixed-Income Toolbox User's Guide[P],2005.
[59]MathWorks, MATLAB7.1, Financial Toolbox User's Guide[P],2005.
[60]MathWorks, MATLAB7.1, Financial Time Series Toolbox User's Guide[P],2005.
[61]Oksendal B. K., Stochastic Differential Equations[M], Springer Verlag, Berlin, fourth edn,1995.
[62]Pesaran M. H. and S. M. Potter, Nonlinear Dynamics and Econometrics:An Introduction[J], Journal of Applied Econometrics,1992,7:S1-S7.
[63]Paolo Brandimarte, Numerical Method in Finance-A MATLAB Based Introduction[M], John Wiley&Sons Ltd,2004.
[64]Risbjerg L. and A. Holmlund, Analytical Framework for Debt and Risk Management in Advances in Risk Management of Government Debt[R], OECD, Paris, France,2005.
[65]Schaller H. and S. van Norden, Regime Switching in Stock Market Returns[J], Applied Financial Economics,1997,7:177-191.
[66]Swedish Ministry of Finance, Guidelines for Central Government Debt Management: Decision taken at the Cabinet Meeting 8 November 2001 [R], Discussion paper,2001.
[67]Swedish National Debt Office, Central Government Debt Management-Proposed Guidelines[R], Discussion paper,2001.
[68]Swedish National Debt Office, Central Government Borrowing:Forecast and Analysis[R], Discussion paper,2002.
[69]Teukolsky, Vetterling and Flannery, Numerical Recipes:The Art of Scientific Computing[M], Cambridge University Press, Cambridge, second edn,1992.
[70]Takero Doi,Toshihiro Ihori and Kiyoshi Mitsui,Sustainability, Debt Management and Public Debt Policy in Japan[R],Working Paper, NBER, July 2006.
[71]Vasicek O., An Equilibrium Characterization of the Term Structure[J], Journal of Financial Economics,1977,5:177-188.
[72]Wheeler G., New Zealand's Experience with Autonomous Sovereign Debt Management[R], New Zealand Debt Management Office,1996.
[73]刘黎明.国家自然科学基金项目《财政体制的博弈分析》,G030301,2001.
[74]刘黎明.财政部横向课题《国债金融预警系统》,2001.
[75]刘黎明,刘玲玲.国债发行期限结构的最优化模型[C].全国数量经济年会,2004.
[76]刘黎明.财政体制的理论与模型方法研究[M].北京：首都经济贸易大学出版社,2007：47-57.
[77]刘黎明.财政转移支付的博弈分析[M].北京：中国财政经济出版社,2000.
[78J刘玲玲,冯健身.中国公共财政[M].北京：经济科学出版社,1999.
[79]刘玲玲.公共财政学[M].北京：清华大学出版社,2000.
[80]贾颖,刘黎明.当前我国国债风险与规模的统计分析[J].北京工业大学学报(社会科学版),2001,4.
[81]王宁,刘黎明.金融风险预警系统设计及实现[J].北京工业大学学报(社会科学版),2001,2.
[82]杨义群,钟兴文,胡征月.国债筹资成本优化的数学模型[J].数量经济技术经济研究.2000,10：25-27.
[83]周军民,赵旭,杨义群.国债规模与发行成本优化问题的研究[J].财经研究,2000,4：3-7.
f84]胡祖光.国债期限的比较研究[J].数量经济技术经济研究.2002,5：13-16.
[85]李庆峰.国债期限与成本优化关系研究[J].数量经济技术经济研究.2004,10：158-161.
[86]宋永明,昌忠泽.国债期限结构理论及其在成本管理中的应用[J].财经问题研究.2004,2：19-22.
[87]靳俐.成本控制于结构管理—对我国当前国债问题的相关思考[J].财贸研究,2002,6：83-86.
[88]龙安国.论国债期限结构的优化[J].科技情报开发与经济,2003,5：147-148.
[89]李向东,谢智波.我国国债期限结构优化的理论研究[J].经济体制改革,2003,3：104-107.
[90]张妹妹.合理设计国债期限结构的必要性探索[J].现代管理科学,2005,6：118-119.
[91]杨大楷,杨勇.关于我国国债收益率曲线的研究[J].财经研究,1997,7：14-19.
[92]姚长辉,梁跃军.我国国债收益率曲线的实证研究[J].金融研究,1998,8：12-18.
[93]吴硕思.随机期限结构与国债定价模型[J].中国管理科学,1999,1：13-20.
[94]陈雯,陈浪南.国债利率期限结构：建模与实证[J].世界经济,2000,8：24-28.
[95]谢赤,吴雄伟.基于Vasicek模型和CIR模型中的中国货币市场利率行为实证分析[J].中国管理科学,2002,6：22-25.
[96]郑振龙,林海.中国市场利率期限结构的静态估计[J].武汉金融,2003,3：33-37.
[97]林海,郑振龙.中国利率动态模型模型[J].财经问题研究,2005,9：45-49.
[98]刘金全,郑挺国.利率期限结构的马尔科夫区制转移模型与实证分析[J].经济研究,2006,11：82-91.
[99]朱世武,陈健恒.交易所国债利率期限结构实证研究[J].金融研究,2003,10：63-73.
[100]傅曼丽,董荣杰,屠美曾.动态利率模型估计方法的一个实证检验[J].华中科技大学学报(自然科学版),2005,4.
[101]潘冠中,邵斌.单因子利率模型的极大似然估计[J].财经研究,2004,10.
[102]胡志强,萧毅海.债券定价与即期利率、远期利率关系分析[J].数量经济技术经济研究,2005,1.
[103]贾康,赵全厚.国债适度规模与我国国债的现实规模[J].经济研究,2000,10：58-62.
[104]高培勇.国债管理也是一种重要的宏观经济调控手段[J].金融与经济,1993,4：19-20.
[105]高培勇.论举借国债的经济作用机制[J].经济研究,1996,9：24-31.
[106]高培勇.债务依存度：透视当前中国财政困难状况的一面镜子[J].国际经济评论,1997,5：28-30.
[107]高培勇.调整完善长期建设国债政策和深化财政体制改革[J].经济研究参考,2004,1.
[108]高培勇,温来成.中长期国债偿还机制：现状、预测与政策选择[J].国际经济评论,2004,9：26-29.
[109]李扬.中国国债市场的发展和国债管理政策[J].教学与研究,1997,8：17-21.
[110]李扬.国债规模：在财政与金融之间寻求平衡[J].财贸经济,2003,1：51-57.
[111]王宁.关于我国国债管理目标和方式的几点思考[J].财政研究,2008,12：29-31.
[112]哈维·罗森,特德·盖亚著.郭庆旺,赵志耘译.财政学[M].北京：中国人民大学出版社,2009：444-454.
[113]陈时兴.中国转型期国债的金融分析[M].北京：中国社会科学出版社,2001：89-121.
[114]陈时兴,姚明龙.我国国债规模的金融经济分析[J].数量经济技术经济研究,2001,5：12-16.
[115]平新乔.道德风险与政府的或然负债[J],财贸经济,2000,11.
[116]程萍.积极财政政策下的债务风险及其防范[J].财贸研究,2002,2：60-62.
[117]吴海霞,邢春华,孙婵娟.运用信号分析法建立我国的金融风险预警系统[J].金融论坛,2004,6：51-56.
[118]丛树海,李生详.我国财政风险指数预警方法的研究[J].财贸经济,2004,6：29-35.
[119]约翰·赫尔著.张陶伟译.期权、期货和其他衍生产品[M].北京：华夏出版社,2004：188-197.
[120]赵谦.我国国债的风险和成本管理研究[D].哈尔滨工业大学博士论文,2007：3-10.
[121]王亚芬,梁云芳.我国财政风险预警系统的建立与应用研究[J].财政研究,2004,11：25-27.
[122]高铁梅,王金明,陈飞,梁云芳.中国转轨时期的经济周期波动-理论、方法及实证分析[M].2009：6-72.
[123]高鸿业.西方经济学[M].北京：中国人民大学出版社,2000：720-754.
[124]多恩·布什著.李庆云等校译.宏观经济学[M].北京：中国人民大学出版社,1997：491-512.
[125]刘树成.经济周期与预警系统[M].北京：科学出版社,1990.
[126]董文泉.经济周期波动的分析与预测方法[M].长春：吉林大学出版社,1998.
[127]吴喜之.非参数统计[M].北京：中国统计出版社,1999：1-16.
[128]沃塞曼著.吴喜之译.现代非参数统计[M].北京：科学出版社,2008：100-110.
[129]唐林俊,杨虎,张洪阳.核密度估计在预测风险价值中的应用[J].数学的实践与认识,2005,10：29-35.
[130]易丹辉.数据分析与EVIEWS应用[M].北京：中国统计出版社,2002.
[131]张晓峒.计量经济学软件EVIEWS使用指南[M].天津：南开大学出版社,2004.
[132]高铁梅.计量经济分析方法与建模：EVIEWS应用及实例[M].北京：清华大学出版社,2005.
[133]薛毅,陈立萍.统计建模与R软件[M].北京：清华大学出版社,2007：107-200.
[134]汤银才.R语言与统计分析[M].北京：高等教育出版社,2008：89-97.
[135]张树德.MATLAB金融计算与金融数据处理[M].北京：北京航天航空大学出版社,2008.
[136]王浩,刘黎明.国债发行与国债债务终结策略分析的新视角.首都经济贸易大学学报[J].2010,2：35-40.
[137]王浩.美国国债：下一次世界经济危机的隐患[J].中国物价,2010,3：24-25.
© 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 |