沪深股市风险度量中半参数ES模型的实证检验
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摘要
目前度量预期不足(Expected Shortfall, ES)的风险技术大多基于参数模型,其建模过程避免不了对收益的分布类型做出假定,但这些分布往往与现实相悖。为此,介绍两种重要半参数模型,即CARE模型和CARES模型,并应用我国2007-2016年上证综合指数与深证成分指数的相关数据评估模型优劣。结果表明:CARES模型与CARE模型在度量我国股市风险中都具有较好的效果,但两者比较,CARES模型明显优于CARE模型。因此,CARES模型能作为我国股市风险度量工具中的一个重要补充。
Most risk measurement techniques for expected shortfall are based on parametric models, which doesn't avoid distribution hypothesizes for asset returns. But these assumptions usually contradict with the reality. In view of this, two main semi-parametric models for measuring ES are proposed, they are CARE and CARES models. By applying these models to Shanghai Composite Index and Shenzhen Component Index sample data from 2007 to 2016, the results show that both the two kinds of models perform well, but the CARES model does better than the CARE model. Therefore, the CARES model can be regarded as an important supplement for the risk measurement yield in China's stock markets.
Most risk measurement techniques for expected shortfall are based on parametric models, which doesn't avoid distribution hypothesizes for asset returns. But these assumptions usually contradict with the reality. In view of this, two main semi-parametric models for measuring ES are proposed, they are CARE and CARES models. By applying these models to Shanghai Composite Index and Shenzhen Component Index sample data from 2007 to 2016, the results show that both the two kinds of models perform well, but the CARES model does better than the CARE model. Therefore, the CARES model can be regarded as an important supplement for the risk measurement yield in China's stock markets.
引文
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[28] 熊正德,张帆,熊一鹏.引入WFCM算法能提高信用违约测度模型准确率吗?——以沪深A股制造业上市公司为样本的实证研究[J].财经理论与实践,2018(1): 147-153.
[2] P Anagnostidis,C Varsakelis, et al. Has the 2008 financial crisis affected stock market efficiency? The case of Eurozone[J].Physica A Statistical Mechanics & Its Applications,2016,447:116-128.
[3] Walid Mensi, Khamis Hamed Al-Yahyaee, et al.Time-varying volatility spillovers between stock and precious metal markets with portfolio implications[J].Resources Policy,2017,53:88-102.
[4] Syed Aun R Rizvi, Shaista Arshad. Analysis of the efficiency-integration nexus of Japanese stock market[J]. Physica A: Statistical Mechanics and its Applications, 2017, 470:296-308.
[5] Sajid Ali, Syed Jawad Hussain Shahzad, et al. Stock market efficiency: a comparative analysis of Islamic and conventional stock markets[J]. Physica A: Statistical Mechanics and its Applications,2018,503: 139-153.
[6] 戴方贤,尹力博. 股指期货交易提升了股票市场有效性吗[J].财贸经济,2017(8):36-51.
[7] 张碧馨.股票市场有效性对风险收益关系的动态影响——基于信息交换和信息不对称视角[J].财经问题研究,2017(8):45-51.
[8] 宋献中;禹天寒.审计行业专长与股价崩盘风险——基于客户重要性和内部控制水平的视角[J].湖南大学学报(社会科学版),2017(4):64-70.
[9] Artzner P, Delbaen F, et al. Coherent measures of risk[J]. Mathematical Finance, 1999, 9(3): 203-228.
[10] Rossi G D, Harvey A. Quantile, expectiles and splines[J]. Journal of Econometrics,2009, 150(2): 179-185.
[11] Engle R F, Manganelli S. CAViaR: conditional autoregressive value at risk by regression quantiles[J]. Journal of Business & Economic Statistics, 2004, 22(4): 367-381.
[12] Taylor J W. Estimating value at risk and expected shortfall using expectiles[J]. Journal of Financial Econometrics, 2008, 6(2): 231-252.
[13] Liao Y, Smith D. Estimating expected shortfall using a conditional autoregressive model: CARES[J]. Journal of Financial Econometrics, 2008,6(1):231-252.
[14] Hamidi B, Maillet B, et al. A dynamic autoregressive expectile for time-invariant portfolio protection strategies[J]. Journal of Economic Dynamics and Control, 2014, 46: 1-29.
[15] Hansen B E. Least squares model averaging[J]. Econometrica, 2007, 75(4): 1175-1189.
[16] Xu Q, Liu X, Jiang C et.al. Nonparametric conditional autoregressive expectile model via neural network with applications to estimating financial risk[J]. Applied Stroachstic Models in Business and Industry, 2016, 32(6): 11-33.
[17] 苏辛,周勇.条件自回归expectile模型及其在基金业绩评价中的应用[J].中国管理科学,2013,21(6):23-31.
[18] 黄大山,卢祖帝.中国股市风险CAViaR建模的稳定性分析[J]. 管理评论.2004, 16(5):48-57.
[19] 刘新华,黄大山.中国股市风险CAviaR方法的稳定性分析及其时变建模[J].系统工程理论与实践,2005,25(9):1-9.
[20] 王新宇,宋学锋.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,2008,28(9):46-51.
[21] 张颖,孙和风.中美股票市场风险差异的新解释-收益对市场风险不对称效应的CAViaR模型与实证[J].南开经济研究,2012,5(5):111-120.
[22] Koenker R,Bassett G. Regression quantiles[J]. Econometrica, 1978,46:33-55.
[23] Newey W K, Powell J L. Asymmetric least squares restimation and testing[J]. Econometrica,1987, 55(1):819-847.
[24] Efron B. Regression percentiles using asymmetric square error loss[J]. Statistica Sinica, 1991, 1(1): 93-125.
[25] Yao Q, Tong H. Asymmetric least squares regression and estimation: a nonparametric approach[J]. Nonparametric Statistics, 1996, 6(2-3): 273-292.
[26] McNeil A J, Frey R. Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approach[J]. Journal of Empirical Finance, 2000, 7(3): 271-300.
[27] Efron B, Tibshirani R J. An introduction to the bootstrap[M]. CRC Press, 1994.
[28] 熊正德,张帆,熊一鹏.引入WFCM算法能提高信用违约测度模型准确率吗?——以沪深A股制造业上市公司为样本的实证研究[J].财经理论与实践,2018(1): 147-153.