风险值VaR框架下SPAN风险控制理论与应用研究
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摘要
金融衍生产品市场的功能在于规避、转移和管理风险,然而由于市场中的交易是以保证金方式进行的,因而存在较大的杠杆效应,这使得衍生产品市场蕴含着巨大的风险。作为衍生产品市场风险控制核心工具的保证金,其设置的正确与否对市场是否成功具有重要的作用。对于保证金大小的设置,当前国际上采用基于组合风险的保证金设置方法,其典型代表是标准资产组合风险分析(SPAN)系统,其得到的保证金既能有效控制风险,又能提高交易者资金的利用效率,降低交易成本。而我国交易所目前仍然采用基于策略性的方法,此种方法设置的保证金使得在大部分时间内,投资者保证金被过多占用,机会成本高昂,资金的使用效率低下,保证金的收取不能很好地反映真实的市场风险,难以实现对风险的有效控制。为了对市场风险进行有效的监控,并提高市场的活跃性,我国保证金的设置需要转换到基于组合风险的动态保证金设置上。
本文在当前得到广泛使用的风险计量技术的框架内,对实用性和可操作性已得到国内外金融行业普遍认同的SPAN系统进行深入研究,把当前风险管理领域内的VaR风险计量技术和Copula技术的丰硕研究成果融入到SPAN系统中,解决SPAN系统的输入参数的设置问题。考虑到金融市场的时变属性,本文把重点放在时变风险计量技术上,在对时变VaR风险计量技术和时变Copula技术在应用中的适用性进行论证之后,给出基于时变VaR方法和时变Copula技术的SPAN系统的输入参数的设置方案。另外,考虑到风险计量的目的是为了更好地进行风险管理,因此本文也对风险资本的配置方法进行了介绍。对于本研究的贡献,主要体现在以下几个方面：
首先,本文改进了时变t-Copula模型中的时变相依参数的演化方程。现有国内对Copula的研究大多数停留在静态Copula上,考虑到时变Copula模型能更好地揭示金融市场中随机变量之间的动态本质,因此本文采用时变Copula模型对随机变量之间的相依性进行描述。时变Copula模型的难点在于如何确定时变相依参数的演化方程。本文在前人研究的基础上,采用包含自相关和两个变量累积概率的历史项之差的绝对值的演化方程作为时变t-Copula参数的演化方程,建立了时变t-Copula的新的演化方程,克服了前人所给时变t-Copula演化方程所存在的缺点。实证结果表明,本文提出的新的演化方程对数据的拟合程度优于前人所给的演化方程对数据的拟合程度。
其次,本文解决了SPAN保证金系统输入参数设置的开放性。现有对SPAN保证金系统的研究并没有给出一套比较完整的参数设置方案及其技术实现细节。为了解决SPAN保证金系统的开放性问题,本文采用测度风险的参数、半参数和非参数时变VaR方法和描述相关结构的时变Copula技术,对SPAN系统中的主要输入参数进行设定,给出VaR-SPAN系统中主要输入参数设定方案和设置的具体步骤,解决了其在可操作性方面存在的技术障碍。实例结果表明,本文给出的SPAN系统中与期货组合有关的主要输入参数的设置方案所得的结果是准确而合理的。
接下来,本文把SPAN系统应用于国内期货组合的保证金计算中,实证分析了国内期货组合保证金设置的合理性。国内期货交易所现阶段对期货组合保证金的设置采用静态的方法收取,并没有考虑到合约与合约之间存在的相关性,必然导致对风险的高估。本文通过文中给出的SPAN保证金输入参数设置方案对国内的期货组合的参数进行设置,在此基础上把SPAN系统计算流程运用于我国衍生品期货组合保证金的计算中,通过SPAN系统计算的仿真程序,采用国内期货交易所商品期货组合的实际数据,实证检验了我国以静态方式设定的组合保证金远高于通过SPAN计算流程所得的组合保证金大小。
此外,除了上述三个贡献,本文也对国际上近期的风险配置理论和方法进行了介绍。风险计量的目的在于对风险进行有效管理,而对风险的有效管理则涉及到对基于风险得到的风险资本进行有效的配置。鉴于国内对风险资本配置理论与方法研究的文献的匮乏,本文对风险资本配置近年来的理论研究成果进行了梳理,并对当前最新的理论成果进行了介绍,特别对新近的基于Copula的尾部风险值的资本配置方法进行了介绍,以期为国内资本配置的研究提供参考。
The function of financial derivative market is to avoid, transfer and manage risk. But the transactions in the market are carried out by ways of margin, there is a big leverage effect which makes the derivative market have enormous risk. Whether the design of margin which is the core instrument to control the derivative market risk is reasonable or not is important for the successful of the market. Internationally, there use the margin setting method which based on portfolio risk to set the size of the margin at present. The typical representative is Standard Portfolio Analysis of Risk (SPAN) system. On one hand, there set margin so that it can effectively control risk; on the other hand, it raises the efficiency of usage of traders'fund and reduce transaction costs. In China, exchanges are still use the approach that based on strategic. By this method, the margin of the investment is excessive occupied for most of the time so that it leads to high opportunity cost and inefficient use of funds. The collection of margin can't well reflect the risk of market and it is difficult to achieve effective control of risk. In order to effectively control and monitor the market risk and increase the activity of the market, the setting of margin needs to be converted to a dynamic margin setting based on portfolio risk.
This paper made a deep research on SPAN system, whose applicability and maneuverability generally recognized by financial industry at home and abroad, within the framework of risk measurement techniques which is widely used at present. The paper put the fruitful research achievements of value-at-risk techniques and copula techniques in the field of risk management into the SPAN system, and solve the problem of setting the input parameters of SPAN system. Taking into account the time-varying properties of financial market, the paper focus on the time-varying risk measurement techniques. The paper have demonstrated the applicability in the use of time-varying risk measurement techniques and time-varying copula techniques, and then gave the proposal of setting input parameters of SPAN system based on time-varying value-at-risk method and time-varying copula techniques. In addition, taking into account that the risk measurement is aimed at better risk management, we also introduced some risk capital allocation methods. The contribution of the study is mainly in the following aspect:
First of all, this paper improved the evolution equation of the time-varying dependent parameters in the time-varying t-copula model. The majority of current domestic studies about copula stay in a static copula. Taking into account that time-varying copula model can better reveal the dynamic nature between random variables in the financial markets; this article uses time-varying copula model describing dependencies between the random variables. The difficulty of time-varying copula model is how to determine the evolution equation of the time-varying dependent parameters. Based on previous studies, this paper uses evolution equation which includes autocorrelation and absolute value of the difference between the historical items of two variables'cumulative probability as the evolution equation of the time-dependent t-copula's parameters, sets up a new evolution equation of the time-varying t-copula, and overcomes the shortcomings of the time-varying t-copula's evolution equations given by our predecessors. The empirical result shows that the proposed new evolution equation fits the data better than the evolution equations given by our predecessors.
Secondly, this paper solved openness of the setting of SPAN margin system's input parameters. Existing research on the SPAN margin system did not give a relative complete set program of parameters and its technical implementation details. To address the SPAN margin system's openness questions, this paper adopts parameters, semi-parametric and non-parametric time-varying value-at-risk methods measuring risk and time-varying copula technique describing dependence structure to set SPAN system's main input parameters, gives the setting program and the concrete setting procedures of the VaR-SPAN system's main input parameters, and addresses the technical barriers existing in its operability. Example results show that results from setting program of the major input parameters in the SPAN system relative to futures combination given by this paper are accurate and reasonable.
Next, this paper applied the SPAN system in the margin calculation of domestic futures portfolio and empirically analyzed the reasonability of domestic futures portfolio's margin setting. Domestic futures exchanges at this stage use static method to collect the futures portfolio's margin settings, and don't take into account the correlation existing between contracts which will inevitably lead to overestimation of risk. This paper set domestic futures combinations'parameters by the setting programs of the SPAN margin's input parameters given in the text, on this basis, used the SPAN system's calculating processes in our domestic derivatives, futures portfolios'margin calculations, and using actual data of domestic futures exchanges'commodity futures portfolios, empirically tested the portfolios'margin set by static way is much higher than that calculated by the SPAN processes in our country through the simulation program calculated by the SPAN system.
In addition to the above three contributions, this paper introduced recent theories and methods of risk allocation in the world. Risk measurement is aimed at effective risk management, which is related to effective allocation of risk capital that we get based on risk. Considering the lack of domestic literature about risk capital allocation theories and methods, this paper sorted out the theoretical study literature of risk capital allocation in recent years, and introduced the latest theoretical achievements. Especially, this paper introduced the capital allocation method based on tail value-at-risk and copula which is newly developed.
本文在当前得到广泛使用的风险计量技术的框架内,对实用性和可操作性已得到国内外金融行业普遍认同的SPAN系统进行深入研究,把当前风险管理领域内的VaR风险计量技术和Copula技术的丰硕研究成果融入到SPAN系统中,解决SPAN系统的输入参数的设置问题。考虑到金融市场的时变属性,本文把重点放在时变风险计量技术上,在对时变VaR风险计量技术和时变Copula技术在应用中的适用性进行论证之后,给出基于时变VaR方法和时变Copula技术的SPAN系统的输入参数的设置方案。另外,考虑到风险计量的目的是为了更好地进行风险管理,因此本文也对风险资本的配置方法进行了介绍。对于本研究的贡献,主要体现在以下几个方面：
首先,本文改进了时变t-Copula模型中的时变相依参数的演化方程。现有国内对Copula的研究大多数停留在静态Copula上,考虑到时变Copula模型能更好地揭示金融市场中随机变量之间的动态本质,因此本文采用时变Copula模型对随机变量之间的相依性进行描述。时变Copula模型的难点在于如何确定时变相依参数的演化方程。本文在前人研究的基础上,采用包含自相关和两个变量累积概率的历史项之差的绝对值的演化方程作为时变t-Copula参数的演化方程,建立了时变t-Copula的新的演化方程,克服了前人所给时变t-Copula演化方程所存在的缺点。实证结果表明,本文提出的新的演化方程对数据的拟合程度优于前人所给的演化方程对数据的拟合程度。
其次,本文解决了SPAN保证金系统输入参数设置的开放性。现有对SPAN保证金系统的研究并没有给出一套比较完整的参数设置方案及其技术实现细节。为了解决SPAN保证金系统的开放性问题,本文采用测度风险的参数、半参数和非参数时变VaR方法和描述相关结构的时变Copula技术,对SPAN系统中的主要输入参数进行设定,给出VaR-SPAN系统中主要输入参数设定方案和设置的具体步骤,解决了其在可操作性方面存在的技术障碍。实例结果表明,本文给出的SPAN系统中与期货组合有关的主要输入参数的设置方案所得的结果是准确而合理的。
接下来,本文把SPAN系统应用于国内期货组合的保证金计算中,实证分析了国内期货组合保证金设置的合理性。国内期货交易所现阶段对期货组合保证金的设置采用静态的方法收取,并没有考虑到合约与合约之间存在的相关性,必然导致对风险的高估。本文通过文中给出的SPAN保证金输入参数设置方案对国内的期货组合的参数进行设置,在此基础上把SPAN系统计算流程运用于我国衍生品期货组合保证金的计算中,通过SPAN系统计算的仿真程序,采用国内期货交易所商品期货组合的实际数据,实证检验了我国以静态方式设定的组合保证金远高于通过SPAN计算流程所得的组合保证金大小。
此外,除了上述三个贡献,本文也对国际上近期的风险配置理论和方法进行了介绍。风险计量的目的在于对风险进行有效管理,而对风险的有效管理则涉及到对基于风险得到的风险资本进行有效的配置。鉴于国内对风险资本配置理论与方法研究的文献的匮乏,本文对风险资本配置近年来的理论研究成果进行了梳理,并对当前最新的理论成果进行了介绍,特别对新近的基于Copula的尾部风险值的资本配置方法进行了介绍,以期为国内资本配置的研究提供参考。
The function of financial derivative market is to avoid, transfer and manage risk. But the transactions in the market are carried out by ways of margin, there is a big leverage effect which makes the derivative market have enormous risk. Whether the design of margin which is the core instrument to control the derivative market risk is reasonable or not is important for the successful of the market. Internationally, there use the margin setting method which based on portfolio risk to set the size of the margin at present. The typical representative is Standard Portfolio Analysis of Risk (SPAN) system. On one hand, there set margin so that it can effectively control risk; on the other hand, it raises the efficiency of usage of traders'fund and reduce transaction costs. In China, exchanges are still use the approach that based on strategic. By this method, the margin of the investment is excessive occupied for most of the time so that it leads to high opportunity cost and inefficient use of funds. The collection of margin can't well reflect the risk of market and it is difficult to achieve effective control of risk. In order to effectively control and monitor the market risk and increase the activity of the market, the setting of margin needs to be converted to a dynamic margin setting based on portfolio risk.
This paper made a deep research on SPAN system, whose applicability and maneuverability generally recognized by financial industry at home and abroad, within the framework of risk measurement techniques which is widely used at present. The paper put the fruitful research achievements of value-at-risk techniques and copula techniques in the field of risk management into the SPAN system, and solve the problem of setting the input parameters of SPAN system. Taking into account the time-varying properties of financial market, the paper focus on the time-varying risk measurement techniques. The paper have demonstrated the applicability in the use of time-varying risk measurement techniques and time-varying copula techniques, and then gave the proposal of setting input parameters of SPAN system based on time-varying value-at-risk method and time-varying copula techniques. In addition, taking into account that the risk measurement is aimed at better risk management, we also introduced some risk capital allocation methods. The contribution of the study is mainly in the following aspect:
First of all, this paper improved the evolution equation of the time-varying dependent parameters in the time-varying t-copula model. The majority of current domestic studies about copula stay in a static copula. Taking into account that time-varying copula model can better reveal the dynamic nature between random variables in the financial markets; this article uses time-varying copula model describing dependencies between the random variables. The difficulty of time-varying copula model is how to determine the evolution equation of the time-varying dependent parameters. Based on previous studies, this paper uses evolution equation which includes autocorrelation and absolute value of the difference between the historical items of two variables'cumulative probability as the evolution equation of the time-dependent t-copula's parameters, sets up a new evolution equation of the time-varying t-copula, and overcomes the shortcomings of the time-varying t-copula's evolution equations given by our predecessors. The empirical result shows that the proposed new evolution equation fits the data better than the evolution equations given by our predecessors.
Secondly, this paper solved openness of the setting of SPAN margin system's input parameters. Existing research on the SPAN margin system did not give a relative complete set program of parameters and its technical implementation details. To address the SPAN margin system's openness questions, this paper adopts parameters, semi-parametric and non-parametric time-varying value-at-risk methods measuring risk and time-varying copula technique describing dependence structure to set SPAN system's main input parameters, gives the setting program and the concrete setting procedures of the VaR-SPAN system's main input parameters, and addresses the technical barriers existing in its operability. Example results show that results from setting program of the major input parameters in the SPAN system relative to futures combination given by this paper are accurate and reasonable.
Next, this paper applied the SPAN system in the margin calculation of domestic futures portfolio and empirically analyzed the reasonability of domestic futures portfolio's margin setting. Domestic futures exchanges at this stage use static method to collect the futures portfolio's margin settings, and don't take into account the correlation existing between contracts which will inevitably lead to overestimation of risk. This paper set domestic futures combinations'parameters by the setting programs of the SPAN margin's input parameters given in the text, on this basis, used the SPAN system's calculating processes in our domestic derivatives, futures portfolios'margin calculations, and using actual data of domestic futures exchanges'commodity futures portfolios, empirically tested the portfolios'margin set by static way is much higher than that calculated by the SPAN processes in our country through the simulation program calculated by the SPAN system.
In addition to the above three contributions, this paper introduced recent theories and methods of risk allocation in the world. Risk measurement is aimed at effective risk management, which is related to effective allocation of risk capital that we get based on risk. Considering the lack of domestic literature about risk capital allocation theories and methods, this paper sorted out the theoretical study literature of risk capital allocation in recent years, and introduced the latest theoretical achievements. Especially, this paper introduced the capital allocation method based on tail value-at-risk and copula which is newly developed.
引文
[1]Abegaz, F. and Naik-Nimbalkar, U. V.,2008. Dynamic Copula-Based Markov Time Series. Communications in Statistics-Theory and Methods,37(15):2447-2460
[2]Acerbi, C. and D. Tasche,2002. On the coherence of expected shortfall. Journal of Banking & Finance,26(7):1487-1503
[3]Alexander, C.,2009. Market Risk Analysis:Volume IV:Value-at-Risk Models, John Wiley & Sons Ltd
[4]Artzner, P., Delbaen, F., Eber, J.-M. and Heath, D.,1999. Coherent Measures of Risk. Mathematical Finance,9(3):203-228
[5]Artzner, Ph., Dealben, F., Eber, J.-M. and Heath, D.,1999. Coherent measures of Risk, Mathematical Finance,9(3):203-228
[6]Ausin, M. C. and H. F. Lopes,2009. Time-varying joint distribution through copulas. Computational Statistics & Data Analysis, doi:10.1016/j. csda.2009.03.008
[7]Barges, M., H. Cossette, and E. Marceau,2009. TVaR-based capital allocation with copulas. Insurance:Mathematics and Economics,45:348-361
[8]Barone-Adesi, G. and K. Giannopoulos,2001. Non parametric VaR Techniques: Myths and Realities, Economic Notes,30(2):167-181
[9]Barone-Adesi, G, Bourgoin F., Giannopoulos, K.,1998. Don't look back. Risk,11: 100-104
[10]Barone-Adesi, G, Giannopoulos K., Vosper L.,1999. VaR without correlations for nonlinear portfolios. Journal of Futures Markets,19:583-602
[11]Barone-Adesi, G, K. Giannopoulos, and L. Vosper,2002. Backtesting Derivative Portfolios with Filtered Historical Simulation (FHS). European Financial Management,8(1):31-58
[12]Bartram, S. M., Taylor, S. J. and Wang, Y.-H.,2007. The Euro and European financial market dependence. Journal of Banking & Finance,31(5):1461-1481
[13]Bauwens, L., Laurent, S. and Rombouts, J.,2006. Multivariate GARCH Models:A Survey, Journal of Applied Econometrics,21:79-109
[14]Bauwens, L., Laurent, S. and Rombouts, J. V K.,2006. Multivariate GARCH Models:A Survey. Journal of Applied Econometrics,21:79-109
[15]Baysal, R. E. and Staum, J.,2008. Empirical Likelihood for Value-at-Risk and Expected Shortfall. The Journal of Risk,11(1):3-32
[16]Berkowitz, J.,2001. Testing Density Forecasts, with Applications to Risk Management. Journal of Business & Economic Statistics,19:465-474
[17]Berkowitz, J., Christoffersen, P. and Pelletier, D.,2009. Evaluating Value-at-Risk Models with Desk-Level Data. Management Science:mnsc.1080.0964
[18]Bodoff, N. M.,2007. Capital Allocation by Percentile Layer, Variance Advancing the Science of Risk,3(1):11-30
[19]Boudoukh, J., Richardson, M., and Whitelaw, R. F.,1998. The Best of Both Worlds. Risk,11:64-67
[20]Butler, J. S. and Schachter, B.,1998. Estimating Value-at-Risk with a Precision Measure by Combining kernel Estimation with Historical Simulation. Review of Derivatives Research,1:371-390
[21]Cai, J., Li, H.,2005. Conditional tail expectations for multivariate phase-type distributions. Journal of Applied Probability,42 (3):810-825
[22]Cai, Z. and Wang, X.,2008. Nonparametric Estimation of Conditional VaR and Expected Shortfall. Journal of Econometrics,147(1):120-130
[23]Campbell, S. D.,2005. A Review of Backtesting and Backtesting Procedures. Board of Governors of the Federal Reserve System. Working paper
[24]Chen, J., Peng, L. and Zhao, Y. C.,2009. Empirical Likelihood based Confidence Intervals for Copulas. Journal of Multivariate Analysis,100(1):137-151
[25]Chen, S. X.,2008. Nonparametric Estimation of Expected Shortfall. Journal of Financial Econometrics,6(1):87-107
[26]Chen, X. H. and Fan, Y. Q.,2006a. Estimation and Model Selection of Semiparametric Copula-Based Multivariate Dynamic Models under Copula Misspecification. Journal of Econometrics,135(1-2):125-154
[27]Chen, X. H. and Fan, Y. Q.,2006b. Estimation of Copula-Based Semiparametric Time Series Models. Journal of Econometrics,130(2):307-335
[28]Cherubini, U., Luciano, E. and Vecchiato, W.,2004. Copula Methods in Finance. Wiley
[29]Chiang, T. C., Jeon, B. N., Li, H.,2007. Dynamic Correlation Analysis Of Financial Contagion:Evidence From Asian Markets. Journal of International Money and Finance,26:1206-1228
[30]Chib, S., Nardari, F., Shephard, N,2002. Markov chain Monte Carlo methods for stochastic volatility models. Journal of Econometrics,108:281-316
[31]Chiragiev, A., Landsman, Z.,2007. Multivariate pareto portfolios:Tce-based capital allocation and divided differences. Scandinavian Actuarial Journal, (4):261-280
[32]Chiu, C.-L., Chiang, S.-M., Hung, J.-C. and Chen, Y.-L.,2006. Clearing Margin System in the Futures Markets--Applying the Value-at-Risk Model to Taiwanese Data. Physica A:Statistical Mechanics and its Applications,367:353-374
[33]Christoffersen, P. and Pelletier, D.,2004. Backtesting Value-at-Risk:A Duration-Based Approach. Journal of Financial Econometrics,2(1):84-108
[34]Christoffersen, P.,1998. Evaluating Interval Forecasts. International Economic Review,39:841-862
[35]CME, BOTCC, and CBOT,2001. Review of Standard Portfolio Analysis of Risk (SPAN) Margin System[R], Commodity Futures Trading Commission, U.S.
[36]Cotter, J. and Dowd, K.,2006. Extreme Spectral Risk Measures:An Application to Futures Clearinghouse Margin Requirements. Journal of Banking & Finance,30(12): 3469-3485
[37]Csoka, P., Herings, P. J.-J., Koczy, L. a. Coherent measures of risk froma general equilibriumperspective [J]. Journal of Banking and Finance,2007,31(8): 2517-2534
[38]Dhaene, J., Henrard, L., Landsman, Z., Vandendorpe, A., Vanduffel, S.,2008. Some results on the cte-based capital allocation rule. Insurance:Mathematics & Economics,42 (2):855-863
[39]Didier, T., Mauro, P., Schmukler, S. L.,2008. Vanishing Financial Contagion? Journal of Policy Modeling,30:775-791
[40]Dobric, J. and Schmid, F.,2007. A Goodness of Fit Test for Copulas Based on Rosenblatt's Transformation. Computational Statistics & Data Analysis,51(9): 4633-4642
[41]Dornbusch, R. and Fischer, S.,1980. Exchange Rate and the Current Account, American Economic Review,70:960-971
[42]Duffie D., Pan J.,1997. An Overview of Value-at-Risk, Journal of Derivatives,4, 7-49
[43]Eldor, R., Hauser, S. and Yaari, U.,2008. SPAN Margining of Option Trading:How Accuracy Promotes Efficiency, Working paper, Available at SSRN:http://ssrn. com/abstract=1118039
[44]Embrechts, P., Puccetti, G.,2007. Fast computation of the distribution of the sum of two dependent random variables. Working Paper
[45]Engle R F.,2002. Dynamic Conditional Correlation:A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models, Journal of Business and Economic Statistics,20:339-350
[46]Engle, R. F. and Manganelli, S.,2004. CAViaR:Conditional Autoregressive Value at Risk by Regression Quantiles. Journal of Business & Economic Statistics,22(4): 367-381
[47]Fantazzini, D.,2008. Dynamic Copula Modelling for Value at Risk, Frontiers in Finance and Economics,5(2):72-108
[48]Fermanian, J.-D.,2005. Goodness-of-Fit Tests for Copulas. Journal of Multivariate Analysis,95(1):119-152
[49]Forbes, K. J., Rigobon, R.,2002. No Contagion, Only Interdependence:Measuring Stock Market Comovements. Journal of Finance,57:2223-2261
[50]Furman, E., Landsman, Z.,2005. Risk capital decomposition for a multivariate dependent gamma portfolio. Insurance:Mathematics & Economics,37(3):635-649
[51]Furman, E., Landsman, Z.,2007. Economic capital allocations for non-negative portfolios of dependent risks. In:Proceedings of the 37-th International ASTIN Colloquium, Orlando
[52]Genest, C., Gendron, M. and Bourdeau-Brien, M.,2009. The Advent of Copulas in Finance. The European Journal of Finance. In Press
[53]Genest, C., Quessy, J. F. and Remillard, B.,2006. Goodness-of-Fit Procedures for Copula Models Based on the Probability Integral Transformation. Scandinavian Journal of Statistics,33(2):337-366
[54]Genest, C., Remillard, B. and Beaudoin, D.,2009. Goodness-of-Fit Tests for Copulas:A Review and a Power Study. Insurance:Mathematics and Economics, In Press
[55]Giannopoulos, K. and R. Tunaru,2005. Coherent risk measures under filtered historical simulation. Journal of Banking & Finance,29(4):979-996
[56]Gonzalo, J., Olmo, J.,2005. Contagion versus Flight to Quality In Financial Markets, Working Paper 05-18, Universidad Carlos III Madrid
[57]Gourieroux, C. and Jasiak, J.,2008. Dynamic Quantile Models. Journal of Econometrics,147(1):198-205
[58]Gregoriou, G, N.,2009. The VAR Implementation Handbook:Financial Risk and Measurement, and Modeling, McGraw-Hill
[59]Hong, L. J. and Liu, G,2008. Simulating Sensitivities of Conditional Value at Risk. Management Science:mnsc.1080.0901
[60]Hong, L. J.,2008. Estimating Quantile Sensitivities. Operations Research:opre. 1080.0531
[61]Jadhav, D. and T. V. Ramanathan,2009. Parametric and non-parametric estimation of value-at-risk. The Journal of Risk Model Validation,3(1):51-71
[62]John, F.,2004. Regulation and economic capital spark debate, Risk,17(6):7-19
[63]Jondeau, E. and Rockinger, M.,2006. The Copula-GARCH Model of Conditional Dependencies:An International Stock Market Application. Journal of International Money and Finance,25(5):827-853
[64]Jorion P,1997. Value at Risk:The New Benchmark for Controlling Market Risk [M]. The McGraw-Hill Companies Inc
[65]Kalkbrenner, M.,2005. An axiomatic approach to capital allocation. Mathematical Finance 15(3):25-437
[66]Karolyi G A, Stulz R M.,1996. Why do Markets Move Together? An Investigation of U. S.-Japan Stock Return Comovements. Journal of Finance,51:951-986
[67]Kaye., P.2005. Risk Measurement in Insurance:A Guide to Risk Measurement, Capital Allocation and Related Decision Support Issues. Casualty Actuarial Society Discussion Paper
[68]Kerkhof, J. and Melenberg, B.,2004. Backtesting for Risk-Based Regulatory Capital. Journal of Banking & Finance,28(8):1845-1865
[69]Kim, G., Silvapulle, M. J. and Silvapulle, P.,2007. Comparison of Semiparametric and Parametric Methods for Estimating Copulas. Computational Statistics & Data Analysis,51(6):2836-2850
[70]Kim, H. T.,2007. Estimation and allocation of insurance risk capital. Ph. D. Thesis, University of Waterloo
[71]Kim, S., Shephard, N., Chib, S.,1998. Stochastic Volatility:Likelihood Inference and Comparison with ARCH Models. The Review of Economic Studies,65: 361-393
[72]Klugman, S. A., Panjer, H. H., Willmot, G E.,2008. Loss models:From data to decisions, third ed. In:Wiley Series in Probability and Statistics, John Wiley & Sons Inc., Hoboken, NJ
[73]Kole, E., Koedijk, K., Verbeek, M.,2007. Selecting Copulas for Risk Management. Journal of Banking & Finance,31:2405-2423
[74]Kolev, N., Anjos, U. d. and Mendes, B. V. d. M.,2006. Copulas:A Review and Recent Developments. Stochastic Models,22(4):617-660
[75]Kreps, R.,2005. Riskiness Leverage Models, Proceedings of the Casualty Actuarial Society,92:31-60
[76]Kuester, K., Mittnik, S., Paolella, M. S.,2006. Value-at-Risk Prediction:A Comparison of Alternative Strategies. Journal of Financial Econometrics,4(1): 53-89
[77]Kupiec, P.,1995. Techniques for Verifying the Accuracy of Risk Measurement Models, Journal of Derivatives,3:73-84
[78]Kupiec, P., H,1994. The performance of S&P 500 futures product margins under the SPAN margining system. The Journal of Futures Markets,14(7):789-811
[79]Kupiec, P. H. and White, A. P.,1996. Regulatory competition and the efficiency of alternative derivative product margining systems. The Journal of Futures Markets, 16(8):943-968
[80]Kupiec, P. H.,1994. The Performance of S&P 500 Futures Product Margins under the SPAN Margining System. Journal of Futures Markets,14(7):789-811
[81]Lam, K., Sin, C.-Y. and Leung, R.,2004. A Theoretical Framework to Evaluate Different Margin-setting Methodologies. Journal of Futures Markets,24(2):117-145
[82]Landsman, Z. M., Valdez, E. A.,2003. Tail conditional expectations for elliptical distributions. North American Actuarial Journal,7(4):55-71
[83]Levy, H.,2006. Stochastic Dominance:Investment Decision Making under Uncertainty, Springer,2nd ed
[84]Longin, F. M.,1999. Optimal Margin Level in Futures Markets:Extreme Price Movements. Journal of Futures Markets,19(2):127-152
[85]Ma, C, H., Wong, W, K.,2006. Stochastic Dominance and Risk Measure:A Decision-Theoretic Foundation for VaR and C-VaR, SSRN Working paper
[86]Manganelli, S. and Engle, R. F.,2004. A Comparison of Value-at-Risk Models in Finance, In G Szeg"o (ed.), Risk Measures for the 21st Century, Chichester, UK: Wiley, pp.123-144
[87]Manganelli, S., and R. F. Engle.2004. A Comparison of Value-at-Risk Models in Finance[M]. In G Szeg'o (ed.), Risk Measures for the 21st Century, p123-144. Chichester, UK:Wiley
[88]Masson, P.,1998. Contagion:Monsoonal Effects, Spillovers, and Jumps between Multiple Equilibria, IMF Working Paper, Available at SSRN:http://ssrn. com/abstract=882708
[89]McNeil, A., Frey, R,.2000. Estimation of Tail-Related Risk Measures for Heterocedastic Financial Times Series:an Extreme Value Approach. Journal of Empirical Finance,7:271-300
[90]Meyer, R and Yu, J,2000. BUGS for a Bayesian analysis of stochastic volatility models, Econometrics Journal,3:198-215
[91]Meyers, G,2003. The Economics of Capital Allocation. Thomas J. Bowles Symposium, Insurance Services offices, Inc. http://www. casact. org/research/aria/ Meyers, pdf
[92]Nelsen, R. B.,2006. An Introduction to Copulas. Springer
[93]Nikoloulopoulos, A. K. and Karlis, D.,2008. Copula Model Evaluation Based on Parametric Bootstrap. Computational Statistics & Data Analysis, In Press
[94]Panjer, H. H.,2002. Measurement of risk, solvency requirements and allocation of capital within financial conglomerates. Research Report 01-15, Institute of Insurance and Pension Research, University of Waterloo
[95]Patton,2006a. Modelling Asymmetric Exchange Rate Dependence. International Economic Review,47(2):527-556
[96]Patton, A. J.,2008. Copula-Based Models for Financial Time Series, Handbook of Financial Time Series. eds. T. G. Andersen, R. A. Davis, J.-P. Kreiss and T. Mikosch, in press, Berlin:Springer
[97]Patton, A. J.,2006b. Estimation of multivariate models for time series of possibly different lengths. Journal of Applied Econometrics,21:147-173
[98]Pearson, N. D. and C. Smithson,2002. VaR:The state of play. Review of Financial Economics,11(3):175-189
[99]Pflug G CH,2000. Some Remarks on the Value-at-Risk and the Conditional Value-at-Risk. In "Probabilistic Constrained Optimization:methodology and Applications", Ed. S. Uryasev, Kluwer Academic Pbulishers
[100]Pritsker, M.,2006. The hidden dangers of historical simulation. Journal of Banking & Finance,30:561-582
[101]Rodriguez, J. C.,2007. Measuring Financial Contagion:A Copula Approach. Journal of Empirical Finance,14(3):401-423
[102]Rodriguez, J. C.,2007. Measuring Financial Contagion:A Copula Approach. Journal of Empirical Finance,14(3):401-423
[103]Ruhm D, Mango, D and Kreps, R,2004. Applications of the Ruhm-Mango-Kreps Conditional Risk Charge Algorithm. to appear in ASTIN Bulletin
[104]Scaillet, O.,2004. Nonparametric Estimation and Sensitivity Analysis of Expected Shortfall. Mathematical Finance,14(1):115-129
[105]Sun, W., S. Rachev, and F. J. Fabozzi,2009. A New Approach for Using Lévy Processes for Determining High-Frequency Value-at-Risk Predictions. European Financial Management,15(2):340-361
[106]Tasche, D.,1999. Risk contributions and performance measurement. Working Paper, Technische Universitt Mnchen
[107]Tasche, D.,1999. Risk contributions and performance measurement. Working Paper, Technische Universitt Mnchen
[108]Taylor, J. W.,2008a. Estimating Value at Risk and Expected Shortfall Using Expectiles. Journal of Financial Econometrics,6(2):231-252
[109]Taylor, J. W.,2008b. Using Exponentially Weighted Quantile Regression to Estimate Value at Risk and Expected Shortfall. Journal of Financial Econometrics, 6(3):382-406
[110]Venter, G. G,2006. Adapting Banking Models to Insurer ERM, ERM Symposium
[111]Venter, G. G,2004. Capitall allocation survey with commentary. North American Actuarial Journal,8(2):p.96-107
[112]Wong, W. K.,2008. Backtesting Trading Risk of Commercial Banks using Expected Shortfall. Journal of Banking & Finance,32(7):1404-1415
[113]白保中,宋逢明,朱世武,2009. Copula函数度量我国商业银行资产组合信用风险的实证研究,金融研究,(4)：129-142
[114]柏满迎,孙禄杰,2007.三种Copula-VaR计算方法与传统VaR方法的比较,数量经济技术经济研究,(2)：154-160
[115]鲍建平,2004.国内外期货市场保证金制度比较研究及其启示,世界经济,(12)：65-69
[116]鲍建平,王乃生,吴冲锋,2005.上海期铜保证金水平设计的实证研究.系统工程理论方法应用,14(1)：33-36
[117]蔡惠珍,2003.含股票选择权投资组合风险值之理论与实证-利用对角模型法(Diagonal Model)改良SPAN风险评量系统：硕士论文,国立中山大学：台湾
[118]陈立峰,2007.倒向随机微分方程数值方法与非线性期望在金融中的应用：g-定价机制及风险度量,博士论文：山东大学
[119]程希骏,徐守坤,2008.期货组合保证金模型及其应用,中国科学技术大学学报,38(9)：1109-1112
[120]迟国泰,刘轶芳,凤敬梅,2005.基于牛顿插值原理的期货价格波动函数及保证金随动模型.数量经济技术经济研究,(3)：150-160
[121]迟国泰,王玉刚,汪红梅,2008.基于多元GARCH-VaR的期货组合保证金模型及其应用研究,预测,27(5)：49-57
[122]迟国泰,余方平,王玉刚,刘轶芳,2006.多品种期货组合风险评价模型及其应用研究.系统工程理论与实践,(9)：17-25
[123]戴良安,刘德明,2008.期货与选择权保证金系统之比较研究—回顾与实证.管理与系统,15(3)：497-522
[124]杜子平,闫鹏,张勇,2009.基于“藤”结构的高维动态Copula的构建,数学的实践与认识,39(10)：96-102
[125]范姜欣伶,2002.含选择权组合跨商品间风险值折抵率之研究：硕士论文,国立中山大学：台湾
[126]傅强,邢琳琳,2009.基于极值理论和Copula函数的条件VaR计算,系统工程学报,24(5)：531-537
[127]高全胜,2004.金融风险计量理论前沿与应用,国际金融研究,(9)：71-78
[128]龚朴,黄荣兵,2008.外汇资产的时变相关性分析.系统工程理论与实践,(8)：26-37
[129]龚朴,黄荣兵,2009.次贷危机对中国股市影响的实证分析——基于中美股市的联动性分析,管理评论,2009,21(2)：21-32
[130]韩德宗,王兴锋,楼迎军.2009.期货价格极端波动下谨慎动态保证金水平的设定——基于极值理论的实证研究,管理学报,6(1)：62-90
[131]韩德宗,王兴锋,杨敏敏,楼迎军,2009.基于极值谱风险测度的动态保证金水平设定,管理科学,22(1)：86-94
[132]洪靖华,2000. SPAN对含选择权投资组合风险值计算之理论与实践：硕士论文,国立中山大学：台湾
[133]胡小平,何建敏,2005.非瓦尔拉斯市场下的风险价值,系统工程学报,20(5)：454-458
[134]胡杨梅,2002.标准组合风险分析系统(SPAN)原理及算法研究,载于《大连商品交易所研究报告集-2001》.中国财政经济出版社
[135]建恩泽,高伟,关瑞青,1999. SPAN保证金系统在我国能否实施的实证分析,南开管理评论,(5)：43-46
[136]蒋贤锋,史永东,李慕春,2007.期货市场保证金调整的市场风险控制作用及制度改革——来自大连商品交易所的实证分析,金融研究,320(2)：77-88
[137]李石,卢祖帝,2008. Copula函数在风险价值度量中的应用,管理评论,20(4)：10-16
[138]林宇,魏宇,黄登仕.2008.基于GJR模型的EVT动态风险测度研究,系统工程学报,23(1)：45-51
[139]刘庆富,仲伟俊,华仁海,刘晓星,2007. EGARCH-GED模型在计量中国期货市场风险价值中的应用,管理工程学报,21(1)：117-121
[140]刘小茂,杜红军,2006.金融资产的VaR和CVaR风险的优良估计,中国管理科学,14(5)：1-6
[141]刘小茂,李楚霖,王建华,2003.风险资产组合的均值—CVaR有效前沿(Ⅰ),管理工程学报,17(1),29-33
[142]刘晓星,2009.流动性调整地风险价值度量：基于金融高频数据的实证分析,系统工程理论与实践,29(7)：16-26
[143]刘轶芳,迟国泰,余方平,2005.基于GARCH-EWMA原理的期货交易保证金随动调整模型.中国管理科学,13(3)：6-14
[144]邵锡栋,连玉君,黄性芳,2009.交易间隔、超高频波动率与VaR——利用日内信息预测金融市场风险,统计研究,26(1)：96-102
[145]史永东,武军伟,2009.基于Levy Copula的组合信用衍生品定价模型,财经问题研究,(10)：76-84
[146]司继文,黄荣兵,龚朴,2007.非参数VaR方法在SPAN系统中的应用,武汉理工大学学报(交通科学与工程版),31(4)：664-667
[147]孙彬,杨朝军,于静,2009. Copula函数选择对投资组合压力测试的影响分析,管理科学,22(2)：99-105
[148]唐爱国,秦宛顺,2003.广义随机占优单调一致风险测度和ES—一种新的风险测度概念和指标种新的风险测度概念和指标,金融研究,274(4)：84-93
[149]王春峰,万海晖,张维,2003.金融市场风险测量模型——VaR,系统工程学报,1：68-74
[150]王玉刚,迟国泰,杨万五,2009.基于Copula的最小方差套期保值比率,系统工 程理论与实践,29(8)：1-10
[151]韦艳华,齐树天,2008.亚洲新兴市场金融危机传染问题研究—基于Copula理论的检验方法,国际金融研究,(9)：22-29
[152]魏宇,2006.金融市场的收益分布与EVT风险测度,数量经济技术经济研究,(4)：101-110
[153]魏宇,2008.金融市场典型事实下的风险价值计算及其检验,管理工程学报,22(2)：117-129
[154]魏正红,温松桥,朱力行,2009.基于经验似然的Value-at-Risk模型的评价方法,中国科学A辑：数学,39(3)：373-384
[155]奚炜,2004. SPAN系统与衍生品市场的风险管理[N],期货日报
[156]肖智,傅肖肖,钟波,2008a.基于EVT-BM-FIGARCH的动态VaR风险测度,中国管理科学,16(4)：18-24
[157]肖智,傅肖肖,钟波,2008b.基于EVT-POT-FIGARCH的动态VaR风险测度,南开管理评论,11(4)：100-104
[158]徐国祥,吴泽智,2004.我国指数期货保证金水平设定方法及其实证研究——极值理论的应用.财经研究,30(11)：63-74
[159]杨维强,2006.倒向随机微分方程和非线性期望在金融中的应用：风险度量,定价机制的估计以及期权定价,博士论文：山东大学
[160]姚京,袁子甲,李忠飞,李瑞,2009.VaR风险度量下的beta系数：估计方法和实证研究,系统工程理论与实践,29(7)：27-34
[161]叶五一,缪柏其,2009.基于Copula变点检测的美国次级债金融危机传染分析,中国管理科学,17(3)：1-7
[162]詹原瑞,韩铁,马珊珊,2008.基于Copula函数族的信用违约互换组合定价模型,中国管理科学,16(1)：1-6
[163]张兵,封思贤,李心丹等,2008.汇率与股价变动关系：基于汇改后数据的实证研究,经济研究,(9)：70-81
[164]张金清,李徐,2008.资产组合的集成风险度量及其应用—基于最优拟合 Copula函数的VaR方法,系统工程理论与实践,28(6)：14-22
[165]张术林,杜俊涛,2007.利用Pearson Ⅳ分布估计Value-at-Risk,系统工程理论与实,(3)：112-117
[166]张张张,2007.风险测度一致性的拓展研究,博士论文：上海交通大学
[167]镇磊,尹留志,方兆本,2008.多项式Copula方法对市场相关结构的分析,中国管理科学,12(3)：1-7
[2]Acerbi, C. and D. Tasche,2002. On the coherence of expected shortfall. Journal of Banking & Finance,26(7):1487-1503
[3]Alexander, C.,2009. Market Risk Analysis:Volume IV:Value-at-Risk Models, John Wiley & Sons Ltd
[4]Artzner, P., Delbaen, F., Eber, J.-M. and Heath, D.,1999. Coherent Measures of Risk. Mathematical Finance,9(3):203-228
[5]Artzner, Ph., Dealben, F., Eber, J.-M. and Heath, D.,1999. Coherent measures of Risk, Mathematical Finance,9(3):203-228
[6]Ausin, M. C. and H. F. Lopes,2009. Time-varying joint distribution through copulas. Computational Statistics & Data Analysis, doi:10.1016/j. csda.2009.03.008
[7]Barges, M., H. Cossette, and E. Marceau,2009. TVaR-based capital allocation with copulas. Insurance:Mathematics and Economics,45:348-361
[8]Barone-Adesi, G. and K. Giannopoulos,2001. Non parametric VaR Techniques: Myths and Realities, Economic Notes,30(2):167-181
[9]Barone-Adesi, G, Bourgoin F., Giannopoulos, K.,1998. Don't look back. Risk,11: 100-104
[10]Barone-Adesi, G, Giannopoulos K., Vosper L.,1999. VaR without correlations for nonlinear portfolios. Journal of Futures Markets,19:583-602
[11]Barone-Adesi, G, K. Giannopoulos, and L. Vosper,2002. Backtesting Derivative Portfolios with Filtered Historical Simulation (FHS). European Financial Management,8(1):31-58
[12]Bartram, S. M., Taylor, S. J. and Wang, Y.-H.,2007. The Euro and European financial market dependence. Journal of Banking & Finance,31(5):1461-1481
[13]Bauwens, L., Laurent, S. and Rombouts, J.,2006. Multivariate GARCH Models:A Survey, Journal of Applied Econometrics,21:79-109
[14]Bauwens, L., Laurent, S. and Rombouts, J. V K.,2006. Multivariate GARCH Models:A Survey. Journal of Applied Econometrics,21:79-109
[15]Baysal, R. E. and Staum, J.,2008. Empirical Likelihood for Value-at-Risk and Expected Shortfall. The Journal of Risk,11(1):3-32
[16]Berkowitz, J.,2001. Testing Density Forecasts, with Applications to Risk Management. Journal of Business & Economic Statistics,19:465-474
[17]Berkowitz, J., Christoffersen, P. and Pelletier, D.,2009. Evaluating Value-at-Risk Models with Desk-Level Data. Management Science:mnsc.1080.0964
[18]Bodoff, N. M.,2007. Capital Allocation by Percentile Layer, Variance Advancing the Science of Risk,3(1):11-30
[19]Boudoukh, J., Richardson, M., and Whitelaw, R. F.,1998. The Best of Both Worlds. Risk,11:64-67
[20]Butler, J. S. and Schachter, B.,1998. Estimating Value-at-Risk with a Precision Measure by Combining kernel Estimation with Historical Simulation. Review of Derivatives Research,1:371-390
[21]Cai, J., Li, H.,2005. Conditional tail expectations for multivariate phase-type distributions. Journal of Applied Probability,42 (3):810-825
[22]Cai, Z. and Wang, X.,2008. Nonparametric Estimation of Conditional VaR and Expected Shortfall. Journal of Econometrics,147(1):120-130
[23]Campbell, S. D.,2005. A Review of Backtesting and Backtesting Procedures. Board of Governors of the Federal Reserve System. Working paper
[24]Chen, J., Peng, L. and Zhao, Y. C.,2009. Empirical Likelihood based Confidence Intervals for Copulas. Journal of Multivariate Analysis,100(1):137-151
[25]Chen, S. X.,2008. Nonparametric Estimation of Expected Shortfall. Journal of Financial Econometrics,6(1):87-107
[26]Chen, X. H. and Fan, Y. Q.,2006a. Estimation and Model Selection of Semiparametric Copula-Based Multivariate Dynamic Models under Copula Misspecification. Journal of Econometrics,135(1-2):125-154
[27]Chen, X. H. and Fan, Y. Q.,2006b. Estimation of Copula-Based Semiparametric Time Series Models. Journal of Econometrics,130(2):307-335
[28]Cherubini, U., Luciano, E. and Vecchiato, W.,2004. Copula Methods in Finance. Wiley
[29]Chiang, T. C., Jeon, B. N., Li, H.,2007. Dynamic Correlation Analysis Of Financial Contagion:Evidence From Asian Markets. Journal of International Money and Finance,26:1206-1228
[30]Chib, S., Nardari, F., Shephard, N,2002. Markov chain Monte Carlo methods for stochastic volatility models. Journal of Econometrics,108:281-316
[31]Chiragiev, A., Landsman, Z.,2007. Multivariate pareto portfolios:Tce-based capital allocation and divided differences. Scandinavian Actuarial Journal, (4):261-280
[32]Chiu, C.-L., Chiang, S.-M., Hung, J.-C. and Chen, Y.-L.,2006. Clearing Margin System in the Futures Markets--Applying the Value-at-Risk Model to Taiwanese Data. Physica A:Statistical Mechanics and its Applications,367:353-374
[33]Christoffersen, P. and Pelletier, D.,2004. Backtesting Value-at-Risk:A Duration-Based Approach. Journal of Financial Econometrics,2(1):84-108
[34]Christoffersen, P.,1998. Evaluating Interval Forecasts. International Economic Review,39:841-862
[35]CME, BOTCC, and CBOT,2001. Review of Standard Portfolio Analysis of Risk (SPAN) Margin System[R], Commodity Futures Trading Commission, U.S.
[36]Cotter, J. and Dowd, K.,2006. Extreme Spectral Risk Measures:An Application to Futures Clearinghouse Margin Requirements. Journal of Banking & Finance,30(12): 3469-3485
[37]Csoka, P., Herings, P. J.-J., Koczy, L. a. Coherent measures of risk froma general equilibriumperspective [J]. Journal of Banking and Finance,2007,31(8): 2517-2534
[38]Dhaene, J., Henrard, L., Landsman, Z., Vandendorpe, A., Vanduffel, S.,2008. Some results on the cte-based capital allocation rule. Insurance:Mathematics & Economics,42 (2):855-863
[39]Didier, T., Mauro, P., Schmukler, S. L.,2008. Vanishing Financial Contagion? Journal of Policy Modeling,30:775-791
[40]Dobric, J. and Schmid, F.,2007. A Goodness of Fit Test for Copulas Based on Rosenblatt's Transformation. Computational Statistics & Data Analysis,51(9): 4633-4642
[41]Dornbusch, R. and Fischer, S.,1980. Exchange Rate and the Current Account, American Economic Review,70:960-971
[42]Duffie D., Pan J.,1997. An Overview of Value-at-Risk, Journal of Derivatives,4, 7-49
[43]Eldor, R., Hauser, S. and Yaari, U.,2008. SPAN Margining of Option Trading:How Accuracy Promotes Efficiency, Working paper, Available at SSRN:http://ssrn. com/abstract=1118039
[44]Embrechts, P., Puccetti, G.,2007. Fast computation of the distribution of the sum of two dependent random variables. Working Paper
[45]Engle R F.,2002. Dynamic Conditional Correlation:A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models, Journal of Business and Economic Statistics,20:339-350
[46]Engle, R. F. and Manganelli, S.,2004. CAViaR:Conditional Autoregressive Value at Risk by Regression Quantiles. Journal of Business & Economic Statistics,22(4): 367-381
[47]Fantazzini, D.,2008. Dynamic Copula Modelling for Value at Risk, Frontiers in Finance and Economics,5(2):72-108
[48]Fermanian, J.-D.,2005. Goodness-of-Fit Tests for Copulas. Journal of Multivariate Analysis,95(1):119-152
[49]Forbes, K. J., Rigobon, R.,2002. No Contagion, Only Interdependence:Measuring Stock Market Comovements. Journal of Finance,57:2223-2261
[50]Furman, E., Landsman, Z.,2005. Risk capital decomposition for a multivariate dependent gamma portfolio. Insurance:Mathematics & Economics,37(3):635-649
[51]Furman, E., Landsman, Z.,2007. Economic capital allocations for non-negative portfolios of dependent risks. In:Proceedings of the 37-th International ASTIN Colloquium, Orlando
[52]Genest, C., Gendron, M. and Bourdeau-Brien, M.,2009. The Advent of Copulas in Finance. The European Journal of Finance. In Press
[53]Genest, C., Quessy, J. F. and Remillard, B.,2006. Goodness-of-Fit Procedures for Copula Models Based on the Probability Integral Transformation. Scandinavian Journal of Statistics,33(2):337-366
[54]Genest, C., Remillard, B. and Beaudoin, D.,2009. Goodness-of-Fit Tests for Copulas:A Review and a Power Study. Insurance:Mathematics and Economics, In Press
[55]Giannopoulos, K. and R. Tunaru,2005. Coherent risk measures under filtered historical simulation. Journal of Banking & Finance,29(4):979-996
[56]Gonzalo, J., Olmo, J.,2005. Contagion versus Flight to Quality In Financial Markets, Working Paper 05-18, Universidad Carlos III Madrid
[57]Gourieroux, C. and Jasiak, J.,2008. Dynamic Quantile Models. Journal of Econometrics,147(1):198-205
[58]Gregoriou, G, N.,2009. The VAR Implementation Handbook:Financial Risk and Measurement, and Modeling, McGraw-Hill
[59]Hong, L. J. and Liu, G,2008. Simulating Sensitivities of Conditional Value at Risk. Management Science:mnsc.1080.0901
[60]Hong, L. J.,2008. Estimating Quantile Sensitivities. Operations Research:opre. 1080.0531
[61]Jadhav, D. and T. V. Ramanathan,2009. Parametric and non-parametric estimation of value-at-risk. The Journal of Risk Model Validation,3(1):51-71
[62]John, F.,2004. Regulation and economic capital spark debate, Risk,17(6):7-19
[63]Jondeau, E. and Rockinger, M.,2006. The Copula-GARCH Model of Conditional Dependencies:An International Stock Market Application. Journal of International Money and Finance,25(5):827-853
[64]Jorion P,1997. Value at Risk:The New Benchmark for Controlling Market Risk [M]. The McGraw-Hill Companies Inc
[65]Kalkbrenner, M.,2005. An axiomatic approach to capital allocation. Mathematical Finance 15(3):25-437
[66]Karolyi G A, Stulz R M.,1996. Why do Markets Move Together? An Investigation of U. S.-Japan Stock Return Comovements. Journal of Finance,51:951-986
[67]Kaye., P.2005. Risk Measurement in Insurance:A Guide to Risk Measurement, Capital Allocation and Related Decision Support Issues. Casualty Actuarial Society Discussion Paper
[68]Kerkhof, J. and Melenberg, B.,2004. Backtesting for Risk-Based Regulatory Capital. Journal of Banking & Finance,28(8):1845-1865
[69]Kim, G., Silvapulle, M. J. and Silvapulle, P.,2007. Comparison of Semiparametric and Parametric Methods for Estimating Copulas. Computational Statistics & Data Analysis,51(6):2836-2850
[70]Kim, H. T.,2007. Estimation and allocation of insurance risk capital. Ph. D. Thesis, University of Waterloo
[71]Kim, S., Shephard, N., Chib, S.,1998. Stochastic Volatility:Likelihood Inference and Comparison with ARCH Models. The Review of Economic Studies,65: 361-393
[72]Klugman, S. A., Panjer, H. H., Willmot, G E.,2008. Loss models:From data to decisions, third ed. In:Wiley Series in Probability and Statistics, John Wiley & Sons Inc., Hoboken, NJ
[73]Kole, E., Koedijk, K., Verbeek, M.,2007. Selecting Copulas for Risk Management. Journal of Banking & Finance,31:2405-2423
[74]Kolev, N., Anjos, U. d. and Mendes, B. V. d. M.,2006. Copulas:A Review and Recent Developments. Stochastic Models,22(4):617-660
[75]Kreps, R.,2005. Riskiness Leverage Models, Proceedings of the Casualty Actuarial Society,92:31-60
[76]Kuester, K., Mittnik, S., Paolella, M. S.,2006. Value-at-Risk Prediction:A Comparison of Alternative Strategies. Journal of Financial Econometrics,4(1): 53-89
[77]Kupiec, P.,1995. Techniques for Verifying the Accuracy of Risk Measurement Models, Journal of Derivatives,3:73-84
[78]Kupiec, P., H,1994. The performance of S&P 500 futures product margins under the SPAN margining system. The Journal of Futures Markets,14(7):789-811
[79]Kupiec, P. H. and White, A. P.,1996. Regulatory competition and the efficiency of alternative derivative product margining systems. The Journal of Futures Markets, 16(8):943-968
[80]Kupiec, P. H.,1994. The Performance of S&P 500 Futures Product Margins under the SPAN Margining System. Journal of Futures Markets,14(7):789-811
[81]Lam, K., Sin, C.-Y. and Leung, R.,2004. A Theoretical Framework to Evaluate Different Margin-setting Methodologies. Journal of Futures Markets,24(2):117-145
[82]Landsman, Z. M., Valdez, E. A.,2003. Tail conditional expectations for elliptical distributions. North American Actuarial Journal,7(4):55-71
[83]Levy, H.,2006. Stochastic Dominance:Investment Decision Making under Uncertainty, Springer,2nd ed
[84]Longin, F. M.,1999. Optimal Margin Level in Futures Markets:Extreme Price Movements. Journal of Futures Markets,19(2):127-152
[85]Ma, C, H., Wong, W, K.,2006. Stochastic Dominance and Risk Measure:A Decision-Theoretic Foundation for VaR and C-VaR, SSRN Working paper
[86]Manganelli, S. and Engle, R. F.,2004. A Comparison of Value-at-Risk Models in Finance, In G Szeg"o (ed.), Risk Measures for the 21st Century, Chichester, UK: Wiley, pp.123-144
[87]Manganelli, S., and R. F. Engle.2004. A Comparison of Value-at-Risk Models in Finance[M]. In G Szeg'o (ed.), Risk Measures for the 21st Century, p123-144. Chichester, UK:Wiley
[88]Masson, P.,1998. Contagion:Monsoonal Effects, Spillovers, and Jumps between Multiple Equilibria, IMF Working Paper, Available at SSRN:http://ssrn. com/abstract=882708
[89]McNeil, A., Frey, R,.2000. Estimation of Tail-Related Risk Measures for Heterocedastic Financial Times Series:an Extreme Value Approach. Journal of Empirical Finance,7:271-300
[90]Meyer, R and Yu, J,2000. BUGS for a Bayesian analysis of stochastic volatility models, Econometrics Journal,3:198-215
[91]Meyers, G,2003. The Economics of Capital Allocation. Thomas J. Bowles Symposium, Insurance Services offices, Inc. http://www. casact. org/research/aria/ Meyers, pdf
[92]Nelsen, R. B.,2006. An Introduction to Copulas. Springer
[93]Nikoloulopoulos, A. K. and Karlis, D.,2008. Copula Model Evaluation Based on Parametric Bootstrap. Computational Statistics & Data Analysis, In Press
[94]Panjer, H. H.,2002. Measurement of risk, solvency requirements and allocation of capital within financial conglomerates. Research Report 01-15, Institute of Insurance and Pension Research, University of Waterloo
[95]Patton,2006a. Modelling Asymmetric Exchange Rate Dependence. International Economic Review,47(2):527-556
[96]Patton, A. J.,2008. Copula-Based Models for Financial Time Series, Handbook of Financial Time Series. eds. T. G. Andersen, R. A. Davis, J.-P. Kreiss and T. Mikosch, in press, Berlin:Springer
[97]Patton, A. J.,2006b. Estimation of multivariate models for time series of possibly different lengths. Journal of Applied Econometrics,21:147-173
[98]Pearson, N. D. and C. Smithson,2002. VaR:The state of play. Review of Financial Economics,11(3):175-189
[99]Pflug G CH,2000. Some Remarks on the Value-at-Risk and the Conditional Value-at-Risk. In "Probabilistic Constrained Optimization:methodology and Applications", Ed. S. Uryasev, Kluwer Academic Pbulishers
[100]Pritsker, M.,2006. The hidden dangers of historical simulation. Journal of Banking & Finance,30:561-582
[101]Rodriguez, J. C.,2007. Measuring Financial Contagion:A Copula Approach. Journal of Empirical Finance,14(3):401-423
[102]Rodriguez, J. C.,2007. Measuring Financial Contagion:A Copula Approach. Journal of Empirical Finance,14(3):401-423
[103]Ruhm D, Mango, D and Kreps, R,2004. Applications of the Ruhm-Mango-Kreps Conditional Risk Charge Algorithm. to appear in ASTIN Bulletin
[104]Scaillet, O.,2004. Nonparametric Estimation and Sensitivity Analysis of Expected Shortfall. Mathematical Finance,14(1):115-129
[105]Sun, W., S. Rachev, and F. J. Fabozzi,2009. A New Approach for Using Lévy Processes for Determining High-Frequency Value-at-Risk Predictions. European Financial Management,15(2):340-361
[106]Tasche, D.,1999. Risk contributions and performance measurement. Working Paper, Technische Universitt Mnchen
[107]Tasche, D.,1999. Risk contributions and performance measurement. Working Paper, Technische Universitt Mnchen
[108]Taylor, J. W.,2008a. Estimating Value at Risk and Expected Shortfall Using Expectiles. Journal of Financial Econometrics,6(2):231-252
[109]Taylor, J. W.,2008b. Using Exponentially Weighted Quantile Regression to Estimate Value at Risk and Expected Shortfall. Journal of Financial Econometrics, 6(3):382-406
[110]Venter, G. G,2006. Adapting Banking Models to Insurer ERM, ERM Symposium
[111]Venter, G. G,2004. Capitall allocation survey with commentary. North American Actuarial Journal,8(2):p.96-107
[112]Wong, W. K.,2008. Backtesting Trading Risk of Commercial Banks using Expected Shortfall. Journal of Banking & Finance,32(7):1404-1415
[113]白保中,宋逢明,朱世武,2009. Copula函数度量我国商业银行资产组合信用风险的实证研究,金融研究,(4)：129-142
[114]柏满迎,孙禄杰,2007.三种Copula-VaR计算方法与传统VaR方法的比较,数量经济技术经济研究,(2)：154-160
[115]鲍建平,2004.国内外期货市场保证金制度比较研究及其启示,世界经济,(12)：65-69
[116]鲍建平,王乃生,吴冲锋,2005.上海期铜保证金水平设计的实证研究.系统工程理论方法应用,14(1)：33-36
[117]蔡惠珍,2003.含股票选择权投资组合风险值之理论与实证-利用对角模型法(Diagonal Model)改良SPAN风险评量系统：硕士论文,国立中山大学：台湾
[118]陈立峰,2007.倒向随机微分方程数值方法与非线性期望在金融中的应用：g-定价机制及风险度量,博士论文：山东大学
[119]程希骏,徐守坤,2008.期货组合保证金模型及其应用,中国科学技术大学学报,38(9)：1109-1112
[120]迟国泰,刘轶芳,凤敬梅,2005.基于牛顿插值原理的期货价格波动函数及保证金随动模型.数量经济技术经济研究,(3)：150-160
[121]迟国泰,王玉刚,汪红梅,2008.基于多元GARCH-VaR的期货组合保证金模型及其应用研究,预测,27(5)：49-57
[122]迟国泰,余方平,王玉刚,刘轶芳,2006.多品种期货组合风险评价模型及其应用研究.系统工程理论与实践,(9)：17-25
[123]戴良安,刘德明,2008.期货与选择权保证金系统之比较研究—回顾与实证.管理与系统,15(3)：497-522
[124]杜子平,闫鹏,张勇,2009.基于“藤”结构的高维动态Copula的构建,数学的实践与认识,39(10)：96-102
[125]范姜欣伶,2002.含选择权组合跨商品间风险值折抵率之研究：硕士论文,国立中山大学：台湾
[126]傅强,邢琳琳,2009.基于极值理论和Copula函数的条件VaR计算,系统工程学报,24(5)：531-537
[127]高全胜,2004.金融风险计量理论前沿与应用,国际金融研究,(9)：71-78
[128]龚朴,黄荣兵,2008.外汇资产的时变相关性分析.系统工程理论与实践,(8)：26-37
[129]龚朴,黄荣兵,2009.次贷危机对中国股市影响的实证分析——基于中美股市的联动性分析,管理评论,2009,21(2)：21-32
[130]韩德宗,王兴锋,楼迎军.2009.期货价格极端波动下谨慎动态保证金水平的设定——基于极值理论的实证研究,管理学报,6(1)：62-90
[131]韩德宗,王兴锋,杨敏敏,楼迎军,2009.基于极值谱风险测度的动态保证金水平设定,管理科学,22(1)：86-94
[132]洪靖华,2000. SPAN对含选择权投资组合风险值计算之理论与实践：硕士论文,国立中山大学：台湾
[133]胡小平,何建敏,2005.非瓦尔拉斯市场下的风险价值,系统工程学报,20(5)：454-458
[134]胡杨梅,2002.标准组合风险分析系统(SPAN)原理及算法研究,载于《大连商品交易所研究报告集-2001》.中国财政经济出版社
[135]建恩泽,高伟,关瑞青,1999. SPAN保证金系统在我国能否实施的实证分析,南开管理评论,(5)：43-46
[136]蒋贤锋,史永东,李慕春,2007.期货市场保证金调整的市场风险控制作用及制度改革——来自大连商品交易所的实证分析,金融研究,320(2)：77-88
[137]李石,卢祖帝,2008. Copula函数在风险价值度量中的应用,管理评论,20(4)：10-16
[138]林宇,魏宇,黄登仕.2008.基于GJR模型的EVT动态风险测度研究,系统工程学报,23(1)：45-51
[139]刘庆富,仲伟俊,华仁海,刘晓星,2007. EGARCH-GED模型在计量中国期货市场风险价值中的应用,管理工程学报,21(1)：117-121
[140]刘小茂,杜红军,2006.金融资产的VaR和CVaR风险的优良估计,中国管理科学,14(5)：1-6
[141]刘小茂,李楚霖,王建华,2003.风险资产组合的均值—CVaR有效前沿(Ⅰ),管理工程学报,17(1),29-33
[142]刘晓星,2009.流动性调整地风险价值度量：基于金融高频数据的实证分析,系统工程理论与实践,29(7)：16-26
[143]刘轶芳,迟国泰,余方平,2005.基于GARCH-EWMA原理的期货交易保证金随动调整模型.中国管理科学,13(3)：6-14
[144]邵锡栋,连玉君,黄性芳,2009.交易间隔、超高频波动率与VaR——利用日内信息预测金融市场风险,统计研究,26(1)：96-102
[145]史永东,武军伟,2009.基于Levy Copula的组合信用衍生品定价模型,财经问题研究,(10)：76-84
[146]司继文,黄荣兵,龚朴,2007.非参数VaR方法在SPAN系统中的应用,武汉理工大学学报(交通科学与工程版),31(4)：664-667
[147]孙彬,杨朝军,于静,2009. Copula函数选择对投资组合压力测试的影响分析,管理科学,22(2)：99-105
[148]唐爱国,秦宛顺,2003.广义随机占优单调一致风险测度和ES—一种新的风险测度概念和指标种新的风险测度概念和指标,金融研究,274(4)：84-93
[149]王春峰,万海晖,张维,2003.金融市场风险测量模型——VaR,系统工程学报,1：68-74
[150]王玉刚,迟国泰,杨万五,2009.基于Copula的最小方差套期保值比率,系统工 程理论与实践,29(8)：1-10
[151]韦艳华,齐树天,2008.亚洲新兴市场金融危机传染问题研究—基于Copula理论的检验方法,国际金融研究,(9)：22-29
[152]魏宇,2006.金融市场的收益分布与EVT风险测度,数量经济技术经济研究,(4)：101-110
[153]魏宇,2008.金融市场典型事实下的风险价值计算及其检验,管理工程学报,22(2)：117-129
[154]魏正红,温松桥,朱力行,2009.基于经验似然的Value-at-Risk模型的评价方法,中国科学A辑：数学,39(3)：373-384
[155]奚炜,2004. SPAN系统与衍生品市场的风险管理[N],期货日报
[156]肖智,傅肖肖,钟波,2008a.基于EVT-BM-FIGARCH的动态VaR风险测度,中国管理科学,16(4)：18-24
[157]肖智,傅肖肖,钟波,2008b.基于EVT-POT-FIGARCH的动态VaR风险测度,南开管理评论,11(4)：100-104
[158]徐国祥,吴泽智,2004.我国指数期货保证金水平设定方法及其实证研究——极值理论的应用.财经研究,30(11)：63-74
[159]杨维强,2006.倒向随机微分方程和非线性期望在金融中的应用：风险度量,定价机制的估计以及期权定价,博士论文：山东大学
[160]姚京,袁子甲,李忠飞,李瑞,2009.VaR风险度量下的beta系数：估计方法和实证研究,系统工程理论与实践,29(7)：27-34
[161]叶五一,缪柏其,2009.基于Copula变点检测的美国次级债金融危机传染分析,中国管理科学,17(3)：1-7
[162]詹原瑞,韩铁,马珊珊,2008.基于Copula函数族的信用违约互换组合定价模型,中国管理科学,16(1)：1-6
[163]张兵,封思贤,李心丹等,2008.汇率与股价变动关系：基于汇改后数据的实证研究,经济研究,(9)：70-81
[164]张金清,李徐,2008.资产组合的集成风险度量及其应用—基于最优拟合 Copula函数的VaR方法,系统工程理论与实践,28(6)：14-22
[165]张术林,杜俊涛,2007.利用Pearson Ⅳ分布估计Value-at-Risk,系统工程理论与实,(3)：112-117
[166]张张张,2007.风险测度一致性的拓展研究,博士论文：上海交通大学
[167]镇磊,尹留志,方兆本,2008.多项式Copula方法对市场相关结构的分析,中国管理科学,12(3)：1-7