基于半参数CARE模型的金融市场VaR度量
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摘要
以CAViaR模型为基础,结合Expectile模型,构建半参数CARE模型,度量金融市场的在值风险。选取2003年1月3日至2015年12月31日上证综合指数与深圳成份指数为研究对象,分别采用半参数CARE模型与GARCH模型刻画VaR的波动情况,并运用几类常返检验来评估模型的优劣。结果表明:GARCH模型能更好地刻画深证成份指数1%VaR与上证综合指数5%VaR,而半参数CARE模型能更好地刻画深证成份指数5%VaR与上证综合指数1%VaR。
This paper propose semi-parametric CARE model,which is based on Expectile models and CAViaR models,to estimate VaR.based on the Shanghai composite index(SSEI) and Shenzhen component index(SZSEI) from January 3,2003 to December 31,2015,this paper uses semi-parametric CARE model and GARCH model depict the risk fluctuation respectively,and some backtesting analysis are taken to evaluate the models.Empirical analysis shows that,GARCH model is better in depicting 1%VaR in SZSEI and 5%VaR for SSEI.Meanwhile,semi-parametric CARE model is better in depicting 5%VaR in SZSEI and 1%VaR for SSEI.
This paper propose semi-parametric CARE model,which is based on Expectile models and CAViaR models,to estimate VaR.based on the Shanghai composite index(SSEI) and Shenzhen component index(SZSEI) from January 3,2003 to December 31,2015,this paper uses semi-parametric CARE model and GARCH model depict the risk fluctuation respectively,and some backtesting analysis are taken to evaluate the models.Empirical analysis shows that,GARCH model is better in depicting 1%VaR in SZSEI and 5%VaR for SSEI.Meanwhile,semi-parametric CARE model is better in depicting 5%VaR in SZSEI and 1%VaR for SSEI.
引文
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[3] Kuan C,The J,Hsu Y.Assesing Value at Risk with CARE,the Conditional Autoregressive Expectile Models[J].Journal of Econometrics,2009,150(2).
[4] 胡宗义,万闯,李毅.基于Expectile风险建模的原油价格风险测度研究[J].统计与信息论坛,2018(1).
[5] 钱夕元,张超.基于参数模型的EVaR风险度量计算方法及实证研究[J].经济数学,2012(4).
[6] 谢尚宇,姚宏伟,周勇.基于ARCH-Expectile方法的VaR和ES尾部风险度量[J].中国管理科学,2014(9).
[7] Engle R F,Manganelli S.CAViaR:Conditional Autoregressive Value at Risk by Regressive Quantiles[J].Journal of Business and Economic Statistics,2004,22(4).
[8] NeweyW K,Powell J L.Asymmetric Least Squares Estimation and Testing[J].Econometrica,1987,55(4).
[9] Rossi G D,Harvey A.Quantiles,Expectiles and Splines[J].Journal of Econometrics,2009,152(2).
[10] Taylor J W.Estimating Value at Risk and Expected Shortfall Using Expectiles[J].Journal of Financial Econometrics,2008,6(2).
[11] 王新宇,宋学峰.间接TARCH-CAViaR模型及其MCMC参数估计与应用[J].系统工程理论与实践,2008(2).
[12] Kupiec P H.Techniques for Verifying the Accuracy of Measurement Models[J].Journal of Derivatives,1995(2).
[13] Christoffersen P F.Evaluating Interval Forecasts[J].International Economic Review,1998,39(4).
[14] Bollerslev T.Generalized Autoregressive Conditional Heteroskedasticity[J].Journal of Econometrics,1986,31(3).
[2] Artzner P,Delbaen F,Eber J M,et al.Coherent Measure of Risk[J].Mathematica Finance,1999,9(3).
[3] Kuan C,The J,Hsu Y.Assesing Value at Risk with CARE,the Conditional Autoregressive Expectile Models[J].Journal of Econometrics,2009,150(2).
[4] 胡宗义,万闯,李毅.基于Expectile风险建模的原油价格风险测度研究[J].统计与信息论坛,2018(1).
[5] 钱夕元,张超.基于参数模型的EVaR风险度量计算方法及实证研究[J].经济数学,2012(4).
[6] 谢尚宇,姚宏伟,周勇.基于ARCH-Expectile方法的VaR和ES尾部风险度量[J].中国管理科学,2014(9).
[7] Engle R F,Manganelli S.CAViaR:Conditional Autoregressive Value at Risk by Regressive Quantiles[J].Journal of Business and Economic Statistics,2004,22(4).
[8] NeweyW K,Powell J L.Asymmetric Least Squares Estimation and Testing[J].Econometrica,1987,55(4).
[9] Rossi G D,Harvey A.Quantiles,Expectiles and Splines[J].Journal of Econometrics,2009,152(2).
[10] Taylor J W.Estimating Value at Risk and Expected Shortfall Using Expectiles[J].Journal of Financial Econometrics,2008,6(2).
[11] 王新宇,宋学峰.间接TARCH-CAViaR模型及其MCMC参数估计与应用[J].系统工程理论与实践,2008(2).
[12] Kupiec P H.Techniques for Verifying the Accuracy of Measurement Models[J].Journal of Derivatives,1995(2).
[13] Christoffersen P F.Evaluating Interval Forecasts[J].International Economic Review,1998,39(4).
[14] Bollerslev T.Generalized Autoregressive Conditional Heteroskedasticity[J].Journal of Econometrics,1986,31(3).
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