[1] Engle R F. Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation[J]. Econometrica, 1982, 50(4): 987-1008. [2] Bollerslev T. Generalized autoregressive conditional heteroskedasticity[J]. Journal of Econometrics, 1986, 31(3): 307-327. [3] 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]. The Journal of Finance, 1993, 48(5): 1779-1801. [4] Baillie R T, Bollerslev T, Mikkelsen H O. Fractionally integrated generalized autoregressive conditional heteroskedasticity[J]. Journal of Econometrics, 1996, 74(1): 3-30. [5] Davidson J. Moment and memory properties of linear conditional heteroscedasticity models and a new model[J]. Journal of Business and Economic Statistics, 2004, 22(1): 16-29. [6] Gong X, Lin B. Structural changes and out-of-sample prediction of realized range-based variance in the stock market[J]. Physical A: Statistical Mechanics and its Applications, 2018, 494: 27-39. [7] Pu W, Chen Y, Ma F. Forecasting the realized volatility in the Chinese stock market: further evidence[J]. Applied Economics, 2016, 48(33): 3116-3130. [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] Hansen P R, Huang Z, Shek H H. Realized GARCH: A joint model for returns and realized measures of volatility[J]. Journal of Applied Econometrics, 2012, 27(6): 877-906. [10] Tian S, Hamori S, Modeling interest rate volatility: A Realized GARCH approach[J]. Journal of Banking and Finance, 2015, 61: 158-171. [11] Sharma P, Vipul. Forecasting stock market volatility using Realized GARCH model: international evidence[J]. The Quarterly Review of Economics and Finance, 2016, 59: 222-230. [12] Jiang W, Ruan Q, Li J, Li Y. Modeling returns volatility: Realized GARCH incorporating realized risk measure[J]. Physica A: Statistical Mechanics and its Applications, 2018, 500: 249-258. [13] 王天一,赵晓军,黄卓.利用高频数据预测沪深300指数波动率——基于Realized GARCH 模型的实证研究[J].世界经济文汇,2014,(5):17-30. [14] 唐勇,刘微.加权已实现极差四次幂变差分析及其应用[J].系统工程理论与实践,2013,33(11):2766-2775. [15] 黄友珀,唐振鹏,周熙雯.基于偏t分布realized GARCH模型的尾部风险估计[J].系统工程理论与实践,2015,35(9):2200-2208. [16] 于孝建,王秀花.基于混频已实现GARCH模型的波动预测与VAR度量[J]. 统计研究,2018,35(1):104-116. [17] Ding Z, Granger C W J. Modeling volatility persistence ofspeculative returns: a new approach[J]. Journal of Econometrics, 1996, 73(1): 185-215. [18] Engle R F, Lee G G. A long-run and short-run componentmodel of stock return volatility.In: Cointegration, Causality, and Forecasting[M]. Britain:Oxford University Press,1999. 475-497. [19] Engle R, Rangle J. The spline-GARCH model for low-frequency volatility and its global macro-economic sauses[J]. Review of Financial Studies, 2008, (21): 1187-1222. [20] Engle R F, Ghysels E, Sohn B. Stock market volatility and macroeconomic fundamentals[J]. Review of Economics and Statistics. 2013, 95(3): 776-797. [21] 陈双,冯成骁.基于GARCH族模型的国际石油价格波动性分析[J].统计与决策,2014,(9):139-143. [22] Barndorff-Nielsen O E, Hansen P R, Lunde A, Shephard N. Designing realized kernels to measure the ex-post variation of equity prices in the presence of noise[J]. Econometrica, 2008, 76(6): 1481-1536. [23] 王春峰,郑仲民,房振明.中国股市已实现"核"波动研究[J].北京理工大学学报(社会科学版),2011,13(3):1-15,26. [24] Andersen T G, Bollerslev T, Diebold F X, Ebens H. The distribution of realized stock return volatility[J]. Journal of Financial Economics, 2011, 61(1): 43-76. [25] Barndorff-Nielsen O E , Hansen P R, Lunde A, Shephard N. Realized kernels in practice: trades and quotes[J]. Econometrics Journal, 2009, 12(3): 1-32. |