基于Copula-GH-CoVaR模型的风险溢出效应研究
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摘要
【目的】为了更好地刻画金融资产之间的风险溢出效应,提高风险溢出效应测度的精度和有效性。【方法】通过构建广义双曲线分布下的边缘分布模型刻画金融资产的尖峰、厚尾以及偏斜等特征,在优选出Copula函数基础上,搭建起基于Copula-GH-CoVaR的风险溢出效应模型,然后以国际大宗商品综合价格与中国原油市场价格为例进行实证研究。【结果】实证研究结果显示,构建的风险溢出效应模型能有效刻画国际大宗商品市场与中国原油市场风险特征;随着时间的推移,风险溢出效应也在发生变化,并且和重大经济事件的发生相一致;国际大宗商品市场与中国原油市场存在双向正的风险溢出效应,但这一效应是不对称的。【结论】模型的检验表明,基于Copula-GH-CoVaR的风险溢出效应模型从精度和有效性上都达到了要求。
[Purposes]By constructing the marginal distribution model under the generalized hyperbolic distribution,the skewness and fatness of financial assets are characterized.[Methods]Based on the preferred Copula function,a linkage risk spillover effect model is established between the international bulk commodity price and the market price of Chinese crude oil,attempting to reveal the risk correlation between the international bulk commodity market and the Chinese crude oil market,providing theoretical support for obtaining the international pricing dominance for China's crude oil market and adapting to the international market.[Findings]The empirical results show that the model of linkage risk spillover the article constructed can effectively characterize the risk characteristics of the international bulk commodity markets and the Chinese crude oil market.The risk spillover effect which is consistent with the occurrence of major economic events changes over time.International bulk commodity market and the Chinese crude oil market is a two-way positive risk spillover effect,which is asymmetry.[Conclusions]The test of the model shows that the risk spillover effect model based on Copula-GH-CoVaR meets the requirements of accuracy and validity.
[Purposes]By constructing the marginal distribution model under the generalized hyperbolic distribution,the skewness and fatness of financial assets are characterized.[Methods]Based on the preferred Copula function,a linkage risk spillover effect model is established between the international bulk commodity price and the market price of Chinese crude oil,attempting to reveal the risk correlation between the international bulk commodity market and the Chinese crude oil market,providing theoretical support for obtaining the international pricing dominance for China's crude oil market and adapting to the international market.[Findings]The empirical results show that the model of linkage risk spillover the article constructed can effectively characterize the risk characteristics of the international bulk commodity markets and the Chinese crude oil market.The risk spillover effect which is consistent with the occurrence of major economic events changes over time.International bulk commodity market and the Chinese crude oil market is a two-way positive risk spillover effect,which is asymmetry.[Conclusions]The test of the model shows that the risk spillover effect model based on Copula-GH-CoVaR meets the requirements of accuracy and validity.
引文
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[21]PATTON A.Copula methods for forecasting multivariate time series[J].Handbook of Economic Forecasting,2013,2:899-960.
[22]GIRARDI G,ERGN A T.Systemic risk measurement:multivariate GARCH estimation of CoVaR[J].Social Science Electronic Publishing,2011,37(8):3169-3180.
[23]SUNGUR R B E A.An introduction to Copulas by Roger B Nelson[J].Journal of the American Statistical Association,2000,95(449):334-335.
[24]KUPIEC P H.Techniques for verifying the accuracy of risk measurement models[J].Social Science Electronic Publishing,1995,3(2):73-84.
[2]ARTZNER P.Coherent measures of risk[J].Mathematical Finanee,1999,9(3):203.
[3]ENGEL R F,NG V K.Measuring and testing the impact of news on volatility[J].Journal of Finance,1993,5:1749-1778.
[4]邻建军,张宗益,秦接.ARCH模型在计算我国股市风险价值中的应用研究[J].系统工程理论与实践,2003,23(5):20-25.LINJ J,ZHANG Z Y,QIN J.Application of ARCH model in calculating the value at risk of China’s stock market[J].System Engineering Theory and Practice,2003,23(5):20-25.
[5]KOENKER R,BASSETT G.Robust tests for heteroscedasticity based on regression quantiles[J].Econometrica,1982,50(1):43-61.
[6]BOLLERSLEV T.Generalized autoregressive conditional heteroskedasticity[J].Journal of Ecomometrics,1986,31:307-327.
[7]ANGELIDIS T,BENOS A,DEGIANNAKIS S.The use of GARCH models in VaR estimation[J].Statistical Methodology,2004,1(1):105-128.
[8]TOBIAS A,BRUNNEREIER K M.CoVaR[J].American Economic Review,2016,106(7):1705-1741.
[9]HAKWA B,JGER-AMBROZ·EWICZ M,RDIGER B.Measuring and analysing marginal systemic risk contribution using CoVaR:a copula approach[EB/OL].[2019-03-27].https://arxiv.org/abs/1210.4713.
[10]GIRARDI G,TOLGA E A.Systemic risk measurement:multivariate GARCH estimation of CoVaR[J].Journal of Banking&Finance,2013,37(8):3169-3180.
[11]马理,葛斌.基于宏观审慎的系统重要性商业银行评价与监管[J].金融监管研究,2014(9):12-25.MA L,GE B.Macro prudential systemically important commercial banks’evaluation and supervision[J].Research on Financial Regulation,2014(9):12-25.
[12]王妍,陈守东.尾部极值分布下的系统性金融风险度量及影响因素分析[J].数理统计与管理,2014,33(6):1010-1020.WANG Y,CHENS D.Systematic financial risk measurement and influencing factor analysis under tail extremum distribution[J].Application of Statistics and Management,2014,33(6):1010-1020.
[13]陈九生,周孝华.沪港通背景下沪港股市联动性研究[J].北京理工大学学报(社会科学版),2017,19(2):87-93.CHEN J S,ZHOU X H.Research on the linkage between Shanghai and Hong Kong stock markets under the background of Shanghai and Hong Kong stock exchange[J].Journal of Beijing University of Technology(Social Science Edition),2017,19(2):87-93.
[14]BARNDORFF-NIELSEN O.Exponentially decreasing distributions for the logarithm of particle size[J].Proceedings of the Royal Society of London,1977,353(1674):401-419.
[15]EBERLEIN E,KELLER U.Hyperbolic distributions in finance[J].Bernoulli,1995,1(3):281-299.
[16]FAJARDO J,FARIAS A.Generalized hyperbolic distributions and Brazilian data[J].Social Science Electronic Publishing,2004,24(5):249-271.
[17]杨爱军,林金官,刘晓星.基于广义双曲线分布的我国股票市场VaR风险度量研究[J].数理统计与管理,2014,33(4):752-760.YANG A J,LIN J G,LIU X X.Research on VaR risk measurement of China's stock market based on generalized hyperbolic distribution[J].Application of Statistics and Management,2014,33(4):752-760.
[18]王周伟,吕思聪,茆训诚.基于风险溢出关联特征的CoVaR计算方法有效性比较及应用[J].管理评论,2014(4):148-159.WANG Z W,LS C,MAO X C.Effectiveness comparison and application of CoVaR calculation method based on risk spillover correlation characteristics[J].Management Review,2014(4):148-159.
[19]GLOSTEN L R,JAGANNATHAN R,RUNKLE D E.On the relation between the expected value and the volatility of the nominal excess return on stocks[J].Staff Report,1993,48(5):1779-1801.
[20]SKLAR M.Fonctions de RépartitionN Dimensions Et Leurs Marges[J].Publ Inst Statist Univ Paris,1959(8):229-231.
[21]PATTON A.Copula methods for forecasting multivariate time series[J].Handbook of Economic Forecasting,2013,2:899-960.
[22]GIRARDI G,ERGN A T.Systemic risk measurement:multivariate GARCH estimation of CoVaR[J].Social Science Electronic Publishing,2011,37(8):3169-3180.
[23]SUNGUR R B E A.An introduction to Copulas by Roger B Nelson[J].Journal of the American Statistical Association,2000,95(449):334-335.
[24]KUPIEC P H.Techniques for verifying the accuracy of risk measurement models[J].Social Science Electronic Publishing,1995,3(2):73-84.
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