中图分类号：
F830
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参考文献
[1] Tsay R S. Analysis of financial time series[M]. 3rd ed. New Jersey:John Wiley & Sons, 2010.
[2] Artzner P, Delbaen F, Eber J, et al. Thinking coherently[J]. Risk, 1997, 10(11):68-71.
[3] Rockafellar R T, Uryasev S. Optimization of conditional value-at-Risk[J]. Journal of Risk, 2000, 2:21-41.
[4] Engle R F. Autoregressive conditional heteroskedasticity with estimates of the variance of UK ination[J]. Econometrica, 1982, 50:987-1008.
[5] Bollerslev T, Ghysels E. Periodic autoregressive conditional heteroskedasticity[J]. Journal of Business and Economic Statistics, 1996, 14:139-152.
[6] Aknouche A, Bibi A. Quasi-maximum likelihood estimation of periodic GARCH and periodic ARMA-GARCH processes[J]. Journal of Time Series Analysis, 2009, 29:9-45.
[7] Bibi A, Lescheb I. Strong consistency and asymptotic normality of least squares estimators for PGARCH and PARMA-PGARCH models[J]. Statistics and Probability Letters, 2010, 80:1532-1542.
[8] Andersen T G, Bollerslev T. Answering the skeptics:Yes, standard volatility models do provide accurate forecasts[J]. International Economic review, 1998, 39(4):885-905.
[9] 陶利斌, 方兆本, 潘婉彬. 中国股市高频数据中的周期性和长记忆性[J]. 系统工程理论与实践, 2004, 24(6):26-33.Tao L B, Fang Z B, Pan W B. The intraday periodicity and long-memory characters in high-frequency data of China stock market[J]. Systems Engineering——Theory & Practice, 2004, 24(6):26-33.
[10] 郭名媛, 张世英. 基于"已实现"波动的VaR计算及其持续性研究[J]. 西北农林科技大学学报(社会科学版), 2006, 6(6):42-45.Guo M Y, Zhang S Y. Calculating VaR on realized volatility and study on the persistence of VaR[J]. Journal of Northwest A&F University (Social Science Edition), 2006, 6(6):42-45.
[11] 徐正国, 张世英. 调整"已实现"波动率与GARCH及SV模型对波动的预测能力的比较研究[J]. 系统工程, 2008, 22(8):60-64.Xu Z G, Zhang S Y. The comparative research on volatility prediction ability of adjusted realized volatility GARCH model and SV model[J]. Systems Engineering, 2008, 22(8):60-64.
[12] 傅强, 伍习丽. 基于ARFIMA-WRBV-VaR的中国股市风险研究[J]. 西南大学学报(自然科学版), 2013, 35(3):9-15.Fu Q, Wu X L. Risk research for Chinese stock market based on ARFIMA-WRBV-VaR model[J]. Journal of Southwest University (Natural Science Edition), 2013, 35(3):9-15.
[13] Visser M P. GARCH parameter estimation using high-frequency data[J]. Journal of Financial Econometrics, 2011, 9:162-197.
[14] 黄金山, 陈敏. 基于高频数据的GARCH模型的伪极大指数似然估计[J]. 应用数学学报(中文版), 2014, 37(6):1005-1017.Huang J S, Chen M. Qusi-maximum exponential likelihood estimators for GARCH models based on high frequency data[J]. Acta Mathematicae Applicatae Sinica (Chinese Series), 2014, 37(6):1005-1017.
[15] Berkes I, Horvath L, Kokoszka P. GARCH processes:Structure and estimation[J]. Bernoulli, 2003, 9:201-207.
[16] Bollerslev T. Generalized autoregressive conditional heteroskedasticity[J]. Journal of Econometrics, 1986, 31:307-327.
[17] Kupiec P H. Techniques for verifying the accuracy of risk measurement models[J]. The Journal of Derivatives, 1995, 3(2):73-84.
[18] Christoffersen P F. Evaluating interval forecasts[J]. International Economic Review, 1998, 39(4):841-862.
[19] Iqbal F, Mukherjee K. A study of value-at-risk based on M-estimators of the conditional heteroscedastic models[J]. Journal of Forecasting, 2012, 31:377-390.
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脚注
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基金
国家自然科学基金面上项目（71673315）；北京工商大学两科基金培育项目（LKJJ2016-03）；首都流通业研究基地项目（JD-YB-2017-021）
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