中国商品期货市场风险管理机制研究
详细信息    本馆镜像全文|  推荐本文 |  |   获取CNKI官网全文
摘要
本文基于中国商品期货市场风险管理机制这一核心问题,结合我国商品期货市场发展的客观实际,从我国商品期货市场系统性风险防范、期货市场投资者风险防范、期货交易所风险防范以及市场风险预警四个方面出发,研究我国商品期货市场价格波动下的市场风险度量、投资者最优套期保值比的确定、期货交易所合理的保证金水平的设置、以及利用影响我国商品期货市场价格波动的因素建立风险预警模型等多个问题。
     我国经济的高速发展对重要的商品资源如粮食、有色金属等的需求日渐增多,大宗商品价格的剧烈波动导致商品市场充斥着巨大的风险,合理的防范期货市场风险迫在眉睫。由于我国期货市场发展不成熟,且均为商品期货,风险分散功能不强,造成期货风险事件屡屡发生,市场风险管理已成为我国商品期货市场健康发展的核心内容之一,有关商品期货市场风险问题的研究具有重大的现实意义。随后,本文对国内外有关期货市场风险度量、套期保值以及保证金设置等的相关文献进行了回顾与总结,在此基础上,阐述了本文的研究思路和创新所在。
     本文对我国商品期货市场风险的特征进行了分析,并对我国商品期货市场风险进行了实证检验,以此为后续研究我国商品期货市场风险的防范做铺垫。首先对中国商品期货市场的发展进行了概述,主要从三个阶段阐述我国商品期货市场的发展历程。随后分析了我国商品期货市场的特点、市场风险成因等问题,并对近几年来我国商品期货市场发生的主要风险事件进行了概述。在此基础上,利用我国商品期货市场主要期货品种的相关数据,对期货价格波动的特征进行了实证检验,研究表明我国商品期货价格的波动具有较强的积聚性和杠杆效应,此外对我国期货市场的分析特征进了市场分析,利用R/S方法实证研究表明我国商品期货市场沪铜、连豆以及郑麦的Hurst指数均不等于0.5,证实了我国期货市场分形结构特征和市场风险的存在。
     通过对期货市场风险进行度量,探讨我国商品期货市场系统性风险防范问题。本文对市场风险度量模型进行了简要的回顾,并提出了本文度量期货价格波动的理论模型-修正的VaR计算模型。VaR方法当前已经成为国际上通行的最重要的风险测量工具之一,并在度量市场风险中的具有良好的表现,本文引入VaR方法并运用GARCH类模型与极值理论(EVT)对我国商品期货市场风险进行了度量。由于在99%的置信水平下,基于GARCH-GED模型估计的VaR值对市场风险估计不足,而GPD模型下的VaR值又存在高估市场风险的现象,由此本文结合GARCH-GED模型和GPD模型构建了修正的VaR计算模型,实证表明基于修正的VaR值能够很好的度量我国期货市场风险。
     投资者是期货市场的主体,防范期货市场风险投资者主要依靠套期保值,本文基于确定最优套期保值比是投资者防范期货市场风险的重要途径,主要围绕如何求解最优套期保值比(Optimal Hedge Ratio)这一关键问题展开。首先分析了期货市场套期保值的涵义,并对有关套期保值风险的研究进行了简要的概述。在此基础上,利用传统的Ederington回归模型、现期货价格具有协整关系的误差修正(ECM)模型,以及具有时变最优套期保值比的状态空间模型和Kalman滤波估计等方法,实证测度了我国铜期货的最优套期保值比,研究表明本文基于状态空间模型的套期保值比卡尔曼滤波估计能有效的提高套期保值的效果,从而能更好的为投资者防范期货市场风险。
     期货交易所和期货公司防范市场风险主要通过保证金作用的发挥,因此确定合理的保证金水平,能有效的提高期货交易所和期货公司防范期货市场风险的能力,因此本文主要分析我国商品期货市场保证金的设置问题。首先对保证金制度以及我国商品期货市场的保证金现状进行了阐述,在此基础上,通过预期的GK方程对保证金调整与期货市场波动性之间的关系进行了实证检测。其次本文拟引入EWMA模型来对我国商品期货市场合理的保证金水平进行了实证测度。EWMA模型最关键的是确定合适的衰减因子λ,而当前λ的确定主要是以RMSE最小化准则,本文试图通过寻找多种衰减因子确定的方式求解最优保证金水平。实证结果表明我国期货市场现行的保证金的制度有待进一步改进,主要表现在收取的保证金整体偏高、各个期货品种保证金收取的方式没有差异性,当前我国的保证金设置方式和水平加大了投资者的市场成本,本文提出了我国期货市场合理的保证金水平。
     全方位、多角度的监测影响我国商品期货市场价格波动的因素,建立市场风险预警模型是防范市场风险的重要途径。本文主要利用影响我国商品期货市场风险的因素,对我国商品期货市场风险的预警管理进行了初步的探讨。通过一定的原则和方法,选取影响我国商品期货价格波动的多个因素,构建了体现我国商品期货市场风险的指标体系。在此基础上利用因子分析的方法,确定了影响我国商品期货市场风险的主要因子,再利用Logistics模型对我国商品期货的市场风险进行了预测,实证表明本文构建的风险预警模型能有效的预防我国商品期货的市场风险,具有一定的实践价值。
     最后,本文从两个方面对本文的研究进行了总结,首先是对本文研究的主要内容和结论进行了阐述。其次,对本文的研究的不足之处进行了分析,并对本文的进一步研究进行了展望。
In this paper,based on the core issue that risk management mechanism of China's commodity futures market, From the beginning of systemic risk prevention, investor risk prevention, futures exchange risk prevention,and risk warning,to analysis of four themes: price volatility risk measure, determination of the optimal hedging ratio, set a reasonable level of margin, risk warning model construction.This paper is divided into seven chapters, the main contents and conclusions of each chapter are described as follows.
     The introduction mainly talking about the research background and significance. At present, China's futures market all are commodity futures, along with rapid economic development, on the growing demand for key commodities such as food, non-ferrous metal,etc, the whole market is full of great risks as the price volatility in recent years, therefore,play the futures market hedging and price discovery role is very important.However, China's futures market is immature, the risk has occurred frequently, risk management has become one of the core for the healthy development of China's commodity futures market.So, research commodity futures market risk problem has positive significance. Then,the studies about the risks of commodity futures markets at home and abroad, the technology courses and innovation of this article are reviewed and summarized.
     In this paper analyzes the development of China's commodity futures market and the market risk. and using data of the main futures to empirical analysis the price fluctuations of China's commodity futures market. The study suggests that the volatility of commodity prices in China's futures market have the ARCH effect and leverage effect, and confirmed the commodity futures market in China has great market risk.
     Analyzes the issue of systemic risk prevention, and measure the risk in the futures market measure.First analyzes the risks aboute the price forecasting, and then introduced a variety method of measuring price volatility. Currently VaR has become the internationally accepted methods of the most important risk measurement tools, introduction of VaR methods and use of GARCH type models and extreme value theory(EVT) to measure China Commodity Futures Market risk. As the VaR-GARCH-GED model underestimated the risk, VaR-GPD model overestimated the risk under 99%confidence level, Therefore, this paper use GARCH-GED model and GPD model calculation the modified VaR, empirical research indicates that the value of using a modified VaR can be a good measure of China's futures market risk.
     Analyzes the market risk prevention of investor, the optimal ratio is one of the most important ways to reduce the risk of futures market. So this chapter on how to solve the key issue of the optimal hedge ratio. First analyzes the meaning and risk of hedging, and then summarizes the studies of hedging. On this basis, using Ederington regression model (OLS model), ECM model and State space model (Sspace)and Kalman filter estimation, empirical measurement the optimal hedging ratio of copper futures, studies show that hedging of State space model and Kalman filter can effectively improve the effect of hedging.
     Futures exchanges and futures companies to avoid market risk mainly through margin setting, so to determine a reasonable level of margin, can effectively improve the ability to avoid the risk of the futures market of the futures exchanges and futures companies, so this chapter analysis of the issue of margin setting of commodity futures market. First analyzes China's commodity futures markets margin, and empirical testing the relationship between margin adjustment and futures market volatility through expected GK equation. This chapter under the form of different distributions of EWMA model to determine the level of China's futures market margin. The key of EWMA is to determine the appropriate decay factor(λ), the traditional method is based on minimizing the RMSE, however, in his paper decay factor will be determined through a variety of ways. In addition, a comparative study based on the normal distribution and Laplace distribution of the EWMA model, Evidence shows that EWMA model based on Laplace distribution can effectively determine margin level of China commodity futures market.
     Comprehensive, multi-angle monitor the factors of impact the price volatility of China's commodity futures market, and establishment of early warning market risk model is an important way to avoid market risks. This chapter is a comprehensive analysis to establish a risk early warning model.Selected a number of factors through certain principles and methods,and then build the risk index system to reflects the risk of China commodity futures market. On this basis, using factor analysis method, select the main factor to analysis the risk of China commodity futures market risks,and then using Logistics model to predict the risk. Empirical evidence shows that the risk of early-warning model constructed in this paper can effectively prevent the risk of commodity futures markets, and has some practical value.
     The final chapter is the end of this article, mainly to illustrate the main contents and conclusions, study shortcomings and further study of this article were discussed.
引文
①资料来源:《中国证券期货统计年鉴2009》。
    ①分别是1913年发表的《Good and Bad Trade:An Inquiry into the Causes of Trade Fluctuation》和1928年发表的《Trade and Credit》的两篇文章。
    ①Pickand(1975)、Balkema, de Haan(1974)的研究都证实了这一观点。
    ②Embrechts, P., Kluppelberg, C., Mikosch, T.,1997. Modelling Extremal Events for Insurance and Finance. Springer, Berlin.
    ①童宛生,管炎彬.期货市场流动性研究.中国期货业协会2001年重点课题,2002,12。
    ①Granger C W, Newbold P.Spurious regressions in econometrics[J].Journal of Econometrics.1974,2(2):111-120.
    ①魏永忠,吴绍忠.浅谈我国社会安全与稳定预警等级模型的建立[J].公安研究,2007(1):32-38.
    ①高辉.中国人民币汇率升值及对期货市场影响的研究.浙江中大期货公司研究报告,第9页。
    [1]A.J, Shcwartz, A.weissi.Credit Rationing in Markets with Imperfect Information[J]. American Economic Review,1981(6),vol71.
    [2]Alexandre,G,Baptism,A.CDaR as a measure of risk:implications for portfolio selection[J].University of Minnesota& University of Arizona,2003(2):18-22.
    [3]Alexandra K B, Mayer J.Computational aspects of minizing conditional value at risk[J]. ComPutaion Management Science,2006(3):3-27.
    [4]Alexei Chekhlov, Stanislav Uryasev, Michael Zabarankin.Draw Down Measure in Portfolio Optimization[J].Research Report,2003(5):11-12.
    [5]Alexei Chekhlov,Stanislav Uryasev,Michael Zabarankin.Draw-down Measure in Portfolio Optimization, Research Report[J].2003(5):11-12.
    [6]Alizadeh, A.&N.Nomikos.A Markov regime switching approach for hedging stock indices[J] Journal of Futures Markets,2004(7):649-74.
    [7]Andersson,F,Mausser H,Rosen D,Uryasev S. Credit risk optimization with conditional value at risk criterion [J].Mathematical Programming,2001(89):273-291.
    [8]Artzner P, Delbaen F, Eber J M.Thinking coherently.Risk[J],1997(10):68-71.
    [9]Artzner P, Delbaen F, Eber J H, Heah D.Coherent Measure of Risk[J].Mathematical Finance,1999(9):203-228.
    [10]Baillie, R.T.&R.J.Myers.Bivariate GARCH Estimation of the Otimal Commodity Futures Hedge[J] Journal of Applicd Econometrics,1991(6):109-124.
    [11]Bali T G.An Extreme Value Approach to Estimating Volatility and Value at Risk[J].Journal of Business,2003(76):83-108.
    [12]B.V.de.Melol Mendecs, R.P.Camara Leal.Maximun Drawdown:Models and Application[J].The Journal of Alternative Investments,2004(2):83-91.
    [13]Beder T.VaR Seductive but dangerous[J].Financial Analysts Journal,1995(9):12-24.
    [14]Best P.Implementing value-at-risk[M].New york:John wiley & Sons, Inc,1998.
    [15]Bollerslev, T.R.F.Engle&J.M.Wooldridge.Acapital asset pricing model with time-varying covarianees[J].The Journal of Political Economy,1988(96):116-131.
    [16]Cathy W S Chen, F.C.Liu, Mike K.P.SO. Heavy-Tailed-Distributed Threshold Stochastic Volatility Models in Finacial Time Series [J].Australian & New Zealand Journal of Statistics,2008(1):29-51.
    [17]Cecchetti.Estimation of the optimal futures hedge[J].Review of Economics and Statistics,1988(70):623-630.
    [18]Chen.C.W.S,So.M.K.P.On a Threshold Heteroscedastic Model[J].Foreca sting, 2006(22):73-89.
    [19]Chih-Chiang, Hsu, Chih-Ping, Tseng&Yaw-Huei, Wang.Dynamic Hedgins, with Futures:A Copula-based GARCH Model[Z].workingpaper,2007.
    [20]Chou, R.Y.Forecasting financial volatilities with extreme values:the Conditional AutoRegressive Range(CARR)Model[J] Journal of Money Credit and Banking, 2005(37):561-582.
    [21]Costello, A.Asem, E.Gardner, E.Comparison of historically simulated VaR:Evidence from oil prices[J]. Energy Economics 2008(2):2154-2166.
    [22]Denis Pelletier.Regime Switehing for Dynamic Correlations.Working paper.2004.
    [23]De.Ville&de.Goyet.Comparing Conditional Hedging Strategies[Z].Working Paper, 2007.9
    [24]Du Mouchel W.M. Estimating the Stable Index-in Order to Measure Tail Thickness: A Critique[M]. Annals of Statistics 11.1983:1019-1031.
    [25]Dowd K.Beyond value-at-risk[M].New York:John Wiley & Sons, Inc,1998.
    [26]E.Acar,S.James. Maximum loss and maximum drawdown in financial markets[M]. London, UK:Conference on Forecasting Financial Markets,1997.
    [27]Engle R.Autoregressive Conditional Heteroscedasticity With Estimates of the Variance of UK Inflation[J]. Econometric,1982(4):987-1008.
    [28]Engle, R.F&K.F.Kroner.Multivariate simultancous generalized ARCH[J]. Econome-tric Theory,1995(11):122-150.
    [29]Engle, R.F., Dynamic Conditional Correlation:A simple class of multivariate generalized autoregressive conditional heteroskedasticity models[J].Journal of Business and Economic Statistics,2002(20):339-350.
    [30]Fama, E. F. and French, K. Commodity futures prices:Some evidence on forecast power, premiums and the theory of storage[J]. The Journal of Business,1987(60): 55-73.
    [31]Fan, Y, Zhang, Y.J., Tsai, H.T.,. Estimating'Value at Risk'of crude oil price and its spillover effect using the GED-GARCH [J]. EnergyEconomics,2008(3):3156-3171.
    [32]F.H.,Risk,Uncertainty,and Profit, Boston:Houghton Mifflin Co.,1921.
    [33]Giorgios.Measure of Risk[J] Journal of Banking & Finance,2002(26):1253-1272.
    [34]Granger C W, Newbold P.Spurious regressions in econometrics [J]. Journal of Econometrics.1974 (2):111-120.
    [35]Harlow W V.Asset allocation in a downside risk framework[J]. Financial Analysts Journal,1991 (9):28-40.
    [36]Hamilton, James, D.&R.Susmel.Autoregressive Conditional Heteroskedasticity and Changes in Regime[J]. Journal of Econometrics,1994(64):307-333.
    [37]Jon Danielsson,Casper GDe Vries.Value-at-Risk and extreme returns[J].Annales deconomie et de statistique,2000,(60):239-270.
    [38]Jorion P.Value-at-Risk:The new benchmark for controlling market risk[M].The MeGraw-hill Companies, Inc,1997.
    [39]Konno H.piecewise Linear risk functions and portfolio optimization.Journal of the Operations Research Society of Japan[J].1990(33):139-156.
    [40]Konno H Birge.Mean Absolute Deviation Portfolio Optimization Model and Its Applications to Tokyo stock market[J].Management Seience,1991 (37):519-529.
    [41]Krokhmal P, Palmquist J, Uryasev S.portfolio optimization with conditional value-at-risk objeetive and constraints[J]. Journal of risk,2002 (4):43-68.
    [42]Kreps,D.Game Theory and Economic Modeling[M]. Oxford University Press.1990.
    [43]Kua-Hwa Chang, Huifen Chen, Chin-Fen Lin. Application of Two-Stage Stochastic Linear Program for Portfoio Selection Problem[J].Lecture Notes in Computer Seienee,2005(3):944-953.
    [44]Laws, J.&J.Thompson.Hedging effectiveness of stock index futures[Z]. Working Paper,2002.
    [45]Leslie A Balzer.Measuring Investment Risk:A Review[J].The Journal of Investing,1995(3):47-58.
    [46]Lien D., Tse, Y.K.&Albert, K.C.Evaluating the Hedging Performance of the Constant Correlation GARCH Model[J].Applied Financial Economics,2002(12):791-980.
    [47]Lien, Donald D.&Li.Yang.Hedging with Chinese Metal Futures[Z].Working Paper, 2006.
    [48]Linderoth J, Shapiro A, Wright S.The empirical behavior of sampling methods for stochtic Programming[J]. Annais of Operations Research,2006(142):215-241.
    [49]Ling S, McAleer M.Asymptotic theory for a vector ARMA-GARCH model [J]. Econometric Theory,2003(19):278-308.
    [50]Lintner J. The valuation of risk assets and the selection of risky investments in stock portfolios and capital budgets [J].Review of Economics and Statistics,1965(47): 13-37.
    [51]Longin.The Asymptonc Distribution of Extreme Stock Market Returns[J]. Journal of Business,1996(6):383-408.
    [52]Mandelbrot,The Variation of certain speculative prices[J].The Journal of Business, 1963 (4):394-419.
    [53]Markowitz H M.Portfolio selection[J].Journal of Finanee,1952(7):77-91.
    [54]Markowitz H M.The Optimization of aquadratie function subject to liner constraints [J]. Naval Researeh Logisties Quarterly,1956(3):111-133.
    [55]Mausser H, and Rosen D.Efficient risk/return frontiers for credit risk[J], Algo Research Quarterly,1999(2):35-47.
    [56]Mckay R, Keekfer T E.VaR is a dangerous technique[J].Corporate Financial Searching for System Integration Supplement.1996(19):30.
    [57]McNeil,A.J.Calculating quantize risk measures for financial time series using extreme value theory,manuscript.Department of Mathematics,ETH,Swiss Federal Technical University.1998,(27):117-137
    [58]McNeil A.J.Extreme Value Theory for Risk Managers.Risk Special Volume,ETH Zurich Preprint,1999
    [59]Meneil, Frey.Estimation of Tail-related Risk Measures for heteroscedastic[J]. Journal of Empirical Finance,2000(7).225-267.
    [60]Michael Mcaleer, Bernardo Da Veiga.Forecasting Value-at-Risk with a Parsimonious Portfolio Spillover GARCH (PS-GARCH) Model [J]. Journal of Forecasting,2008(27):1-19.
    [61]Ming Yuan,LeonLi.Dynamic hedge ratio for stock index futures:application of threshold VECM[J].Applied Economics,2008(08):25-46.
    [62]Misra, L.&K.Sukant.Asymmetry, Risk and Correlation Dynamics in theU.S. Fiber Market[J]. American Agricultural Economics Association,2005(7):24-27.
    [63]Mossin J. Equilibrium in a capital asset market[J].Econometrica,1966(34):768-783.
    [64]Nash, Y Chen, Ray Y Chou, Nathan Liu & Gang Shyy.Estimating Time Varying Hedge Ratios With A Range Based Volatility Model[Z].Working Paper,2007.
    [65]Neftci.Value at risk Calculations Extreme Events and Tail Estimation[J]Journal of Derivatives Spring.2000(2):23-38.
    [66]Nelson.D.B.Conditional heteroskedasticity in asset returns:A new approach [J]. Econometrica,1991(59):347-370.
    [67]Nikolas.T, Hereules.V, Stavros.A.CVaR models with selection hedging internatiolnal Assets al location[J]. Journal of Banking & Finance,2002(6):1535-1561.
    [68]Panigirtzoglou,Skiadopoulos.Autoregressive modeling and money-incomecausality detection.Journal of MonetaryEconomies,2004 (7):85-106.
    [69]Ray, Chou, Chun-Chou, Wu&Nathan, Liu, Forecasting Correlation and Covariance with a Range-Based Dynamic Conditional Correlation Model[J].China International Conference in Finance,2005(7):5-7.
    [70]R flung, GSome Remarks on the Value-at-risk and the Conditional Value-at-Risk.In, Uryasev, S, Probabilistic Constrained Opmization:Methodology and Application [M]. Boston:Kluwer Aeademic Publishers,2000.
    [71]Roekfeller, T.Uasev.Conditional Value-at-Risk for General loss Distribution [J]. Journal of Banking & Finance,1999(4):1445-1471.
    [72]Rockafellar R·T, Uryasev S.Optimization of conditional value-at-risk[J] Journal of Risk,2000(2):21-41.
    [73]Rockfeller T, Uryasev S.Conditional Value-at-Risk for general loss Distribution[J]. Journal of Banking & Finance.2002(2):1445-1471.
    [74]Roy A D.Safety-first and the holding of assets[J].Econometrics,1952(20)431-449.
    [75]Sharpe W A.simplified model for portfolio selection analysis[J].Management Science, 1963(9):277-293.
    [76]Sharpe W A. Capital asset prices:A theory of market equilibrium under conditions of risk[J].Journal of Finance,1964(19):425-442.
    [77]Shawky, H.A., A.M.Marathe&C.L.Barrett.A First Look at the Empirical Relation Between Spot and Futures Eleetricity Prices in the United States[J]. The Journal of Futures Markets,2003,23(10):931-955.
    [78]Stephan Johri,PD.Dr.Diethelm Wiirtc,Dr.kai.Nagel Portfolio Optimization with hedge funds:Conditiona Value at Risk and Conditional Draw-Down at Risk for Portfolio[J].Optimization with Altemative Investments,2004(3):61、78-80.
    [79]Stephan Johri,P D.Dr.Diethelm Wiirtc,Dr.kai.Nagel Portfolio Optimization with hedge funds:Conditional Value At Risk And Conditional Draw-Down At Risk For Portfolio Optimization With Alternative Investments[J].2004(3).156-162.
    [80]Stefano Gatti, Rigamonti, Francesco Saita,Mauro Senati. Measuring Value at Risk in Project Finance Transactions[J]. European Financial Management,2007(1):135-158.
    [81]Tse, Y,A.Tsui.A Multivariate GARCH Model with Time varying Correlations [J].Journal of Business and Economic Statistics,2002(20):351-362.
    [82]Turan G Bali.Risk Measurement Performance of Alternative.The Journal of Risk and Insurance,2008(2):411-437.
    [83]Uryasev S.Conditional Value-at-Risk:Optimization Algorithms and Applications [N].Finicial Engineering News,2000(2):1-5.
    [84]Velayoudoum Marimoutou, Bechir Raggad,Abdelwahed Trabelsi.Extreme Value Theory and Value at Risk:Application to oil market [J]. Energy Economics 31 (2009) 519-530.
    [85]Viswanath, M. Efficient use of information, convergence adjustments, and regression estimates of hedge ratios[J]. The Journal of Futures Markets.1993(13):43-53.
    [86]Yamai Y, Yoshiba T.On the Validity of Value-at-Risk:Comparative Analyses with Expected Shortfall [J].Monetary and Economic Studies,2002(1):57-86.
    [87]Yang, W.M-GARCH Hedge Ratios and Hedging EffeCtiveness in Australian Futures Markets[J].School of Finance and Business Economics,2001(8):89-113.
    [88]Yi Hao, Lai, Chen.Cathy&WS, Gerlaeh.Richard.optimal Dynamic Hedging Using Copula-Threshold-GARCH Models[Z].Working Paper,2006.
    [89]鲍建平.国内外期货市场保证金制度比较研究及其启示[J]世界经济,2004(12):1-5.
    [90]鲍建平,王乃生,吴冲锋.上海期铜保证金水平设计的实证研究[J]·系统工程理论方法应用,2005(1).33-36.
    [91]蔡纯.本次经济危机主要大宗商品期货价格波动性研究.金融理论与实践,2010(2):64-69.
    [92]曹忠忠.股指期货风险测算及监管研究.同济大学硕士毕业论文,2007,中国硕士 学位论文全文数据库.
    [93]常清.中国期货市场发展的战略研究[M].北京,经济科学出版社,2001.2.
    [94]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.
    [95]陈学华,杨辉耀.’VaR-APARCH模型与证券投资风险量化分析[J].中国管理科学,2003(1):22-27.
    [96]迟国泰,王玉刚,汪红梅.基于多元GARCH-VaR的期货组合保证金模型及其应用研究[J].预测,2008(5):49-57.
    [97]崔海波,赵希男,张利兵.一种证券投资风险度量方法的应用研究[J].系统工程,2004(3):88-91.
    [98]丁元子.杠杆效应与沪市在险价值(VaR)的计算基于GARCH-t、EGARCH-t和TGARCH-t模型的比较.统计教育,2009(1):9-13.
    [99]佟德庆.期货市场风险及其监管研究.西北大学博士毕业论文,2005,中国博士学位论文全文数据库.
    [100]范英.VaR方法及其在风险分析中的应用初探[J].中国管理科学,2000(3):26-32.
    [101]范英.股市风险值估计的EWMA方法及其应用[J].预测,2001,20(3):34-37.
    [102]法博齐.资本市场:机构与工具[M].北京:经济科学出版社,1998.56.
    [103]高和.一种复杂系统的预测方法[J].北京航空航天大学学报,1992(1):101-105.
    [104]龚朴,黄荣兵.SPAN保证金系统中的VaR实现技术.系统工程学报,2009(10):523-530.
    [105]郭晓亭.基于GARCH模型的中国证券投资基金市场风险实证研究[J].国际金融研究,2005(10):55-59.
    [106]韩延萌.基于风险价值(VaR)模型在股市中的实证分析闭[J].价值工程,2005(7):32-34.
    [107]何冬黎.基于VaR的我国沪铝期货市场风险度量实证研究.青岛大学硕士毕业论文,2007,中国硕士学位论文全文数据库.
    [108]胡畏.中国期货市场价格波动率的到期效应[J].预测,2000(1):45-46.
    [109]华仁海、仲伟俊.我国期货市场期货价格收益、交易量、波动性关系的动态分析[J].统计研究,2003(7):25-30.
    [110]蒋学雷、陈敏、吴国富.中国股市的羊群效应的ARCH检验模型与实证分析[J].数学的实践与认识,2003(3):56-63.
    [111]李基梅,刘青青.VaR-GARCH模型在我国股指期货风险管理中的应用.山东理工大学学报(自然科学版),2009(4):73-76.
    [112]李婷,张卫国.风险资产组合均值-CVaR模型的算法分析[J].安徽大学学报(自然科学版),2006(11):22-25.
    [113]李燕华.我国证券市场的VaR与CVaR方法比较实证分析[J].山西财经大学学报,2007(4):132-135.
    [114]梁春早.考虑基差非对称效应的期货VaR估计方法研究.统计与决策,2009(7):22-24.
    [115]林孝贵.t分布下期货套期保值VaR风险的敏感度.统计与决策,2008(8):26-28.
    [116]刘俊山.基于风险测度理论的VaR与CVaR的比较研究[J].数量经济技术经济研究,2007(3):125-133.
    [117]刘昆仑.极值理论在VaR和CVaR中的应用及对沪市的实证研究[J].山东教育学院学报,2008(3):84-86、105.
    [118]刘小茂,李楚霖,王建华.风险资产组合的均值-CVaR有效前沿[J].管理工程学报,2005(1):1-5.
    [119]刘艳春,高闯.风险资产组合的均值-WCVaR模糊投资组合优化模型[J].中国管理学,2006(11):16-21.
    [120]刘轶芳,迟国泰,余方平,等.基于GARCH-EWMA的期货价格预测模型[J].哈尔滨工业大学学报,2006,9(38):1572-1575.
    [121]刘云.信息工程基础[M].北京:社会科学文献出版社,2003.
    [122]洛仑兹·格利茨.金融工程学.北京,经济科学出版社,1998.37-38.
    [123]卢玮,期货市场交易保证金设置的理论研究,期货日报,2004,7-1.
    [124]王骏,张宗成.中国有色金属期货市场套期保值绩效的实证研究:2000-2004年[J]. 中国地质大学学报(社会科学版),2006(1):46-51.
    [125]蒋美云.期货市场保值与投机的量化分析[J].经济师,2001(10):107-108.
    [126]彭红枫,叶永刚.基于修正的ECM-GARCH模型的动态最优套期保值比率估计及比较研究[J].中国管理科学,2007(5):29-35.
    [127]乔埃尔.贝西斯著,许世清等译.商业银行风险管理-现代理论与方法[M].海大出版社.2001
    [128]齐明亮.中国期货市场效率实证研究.华中科技大学博士毕业论文,2004,中国博士学位论文全文数据库
    [129]司继文,张明佳,龚朴.基于Monte Carlo模拟和混合整数规划的CVaR(VaR)投资组合优化[J].武汉理工大学学报(交通科学与工程版),2005(6):411-414.
    [130]宋逢明,谭慧.VaR模型中流动性风险的度量[J].数量经济技术经济研究,2004(6):114-123.
    [131]宋春林,曹广福.金融市场的风险值测量及动态分析[J].现代商贸工业,2008(3):153-154.
    [132]童宛生,管炎彬.期货市场流动性研究.中国期货业协会2001年重点课题,2002,12.
    [133]宋曦.我国股指期货保证金设定的方法及实证[J].统计与决策,2006(9):120-121.
    [134]王春峰.金融市场风险管理[M].天津:天津大学出版社,2001.
    [135]王春峰.金融市场测量模型-VaR[J].系统工程学报,2000(1):67-75.
    [136]王春峰,李刚.基于分布拟和的VaR估计[J].管理工程学报,2002(4):33-37.
    [137]王建华,李楚霖.度量与控制金融风险的新方法[J].武汉理工大学学报(信息与管理工程版),2002(4):60-63.
    [138]汪贵浦,王明涛.Harlow下偏矩证券组合优化模型的求解方法研究[J].系统工程理论与实践,2003(6):42-49.
    [139]王莹莹.中国股票市场Hurst指数与多重分形分析.华中科技大学硕士毕业论文,2006,中国硕士学位论文全文数据库.
    [140]王玉玲.CVaR方法在投资组合中的应用[J].统计与决策,2008(2):71-72.
    [141]温红梅,姚凤阁.CVaR在操作风险度量与控制中的应用分析[J].哈尔滨商业大学学报(社会科学版),2008(1):35-33.
    [142]危慧惠小麦期货收益时间序列分析[J].山西财经大学学报,2004(1):109-110.
    [143]韦廷权.风险度量和投资组合的构造的进一步实证[J].南开经济研究,2001(2):3-6.
    [144]魏永忠,吴绍忠.浅谈我国社会安全与稳定预警等级模型的建立[J].公安研究,2007(1):32-38.
    [145]徐绪松.半绝对离差证券组合投资模型[J],武汉大学学报,2002(3):297-300.
    [146]徐国祥.指数期货套期保值实证研究-以香港恒生指数期货为例[J].统计研究,2004(7):49-2.
    [147]徐毅.大连商品交易所大连豆号合约动态保证金研究[J].运筹与管理,2007(1):107-111.
    [148]斯蒂格里茨.经济学阅(上册)[M].北京:中国人民大学出版社,1996,126.
    [149]邵旭东,黄国安.基于VaR模型的股指期货保证金风险管理.浙江金融,2008(5):37-41.
    [150]杨海珍,鄢宏亮.股指期货跨期套利交易保证金设置方法的比较[J].系统工程理论与实践,2008(8):132-138.
    [151]姚刚.金融衍生产品的风险控制与防范[J].经济理论与经济管理,1998(1):49-51
    [152]姚京,李仲飞.基于VaR的金融资产配置模型[J].中国管理科学,2004(12):8-14.
    [153]伊晟.VaR在我国有色金属期常市场风险预测中的应用研究——以铜、铝为例.昆明理工大学硕士毕业论文,2007,中国硕士学位论文全文数据库.
    [154]余方平,许文,迟国泰.基于VaR-WKDE单个期货合约动态基准保证金模型研究.哈尔滨工业大学学报,2009(2):254-256.
    [155]约翰,依特韦尔等.新帕尔格雷夫经济学大辞典[z]第四卷,北京:经济科学出版社,1992.216
    [156]张璐,吴怡平.基于GARCH的VaR模型计算铝期货合约最佳保证金比例.时代金融,2007(9):22-23.
    [157]张凤霞,王宝森.基于植入SV的VaR模型的股指期货风险度量.河北建筑科技学院学报,2006(3):80-83.
    [158]张宗成.期货与期权市场运作方略[M].武汉:华中科技大学出版社,2000.103-109.
    [159]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.
    [160]周蓓,齐中英.对我国期货市场波动性的分阶段实证研究[J].数理统计与管理,2007,(5):518-527.

© 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