[1] Carnero M A, Pena D, Ruiz E. Persistence and kurtosis in GARCH and stochastic volatility models [J]. Journal of Financial Econometrics, 2004, 2(2): 319-342.[2] Corsi F. A simple approximate long-memory model of realized volatility [J]. Journal of Financial Econometrics, 2009, 7(2):174-196.[3] 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.[4] Andersen T G, Bollerslev T, Diebold F X, et al. The distribution of realized stock return volatility [J]. Journal of Financial Economics, 2001, 61(1): 43-76.[5] Andersen T G, Bollerslev T, Diebold F X, et al. Modeling and forecasting realized volatility [J]. Econometrica, 2003, 71(2): 579-625.[6] Koopman S H, Jungbacker B, Hol E. Forecasting daily variability of the S&P 100 stock index using historical, realised and implied volatility measurements [J]. Journal of Empirical Finance, 2005, 12(3): 445-475.[7] Hansen P R, Lunde A. A forecast comparison of volatility models: does anything beat a GARCH(1,1)? [J]. Journal of Applied Econometrics, 2005, 20(7): 873-889.[8] 魏 宇. 沪深300股指期货的波动率预测模型研究[J]. 管理科学学报,2010,13(2):66-76. [9] Lee S S, Mykland P A. Jumps in financial markets: A new nonparametric test and jump dynamics[J]. Review of Financial Studies, 21(6): 2535-2563.[10] Barndorff-Nielsen O E, Shephard N. Power and bipower variation with stochastic volatility and jumps[J]. Journal of Financial Econometrics 2004, 2(1): 1-48.[11] Andersen T G, Bollerslev T, Diebold F X.Roughing it up: Including jump components in the measurement, modeling and forecasting of return volatility[J]. The Review of Economics and Statistics, 2007, 89(4): 701-720.[12] Huang X, Tauchen G. The relative contribution of jumpsto total price variance[J]. Journal of Financial Econometrics, 2005, 3(4): 456-499.[13] 王春峰,姚宁,房振明, 等.中国股市已实现波动率的跳跃行为研究[J]. 系统工程,2008,26(2): 1-6.[14] Shalen C T. Volume, volatility and dispersion of beliefs[J]. Review of Financial Studies, 1993, 6(2): 405-434.[15] Wang J. A model of competitive stock trading volume[J]. Journal of Political Economy, 102(1): 127-168.1994[16] Buraschi A, Trojani F, Vedolin A. The joint behavior of credit spreads, stock options and equity returns when investors disagree[R]. Working Paper, Imperial College, 2007.[17] Corsi F, Pifino D, Reno R. Threshold bipower variation and the impact of jumps on volatility forecasting[J]. Journal of Econometrics, 2010, 159(2): 276-288.[18] Barndorff-Nielsen O E, Kinnebrock S, Shephard N. Measuring downside risk--realised semivariance[J]. In Volatilityand Time Series Econometrics: Essays in Honor of Robert F. Engle, ed. by T. Bollerslev, J. Russell, and M. Watson. Oxford University Press, 2010.[19] Patton A J, Sheppard K. Good volatility, bad volatility: Signed jumps and the persistence of volatility[J]. Unpublished paper: Oxford-Man Institute, University of Oxford, 2011.[20] Ashely R, Granger C W J, Schmalensee R, et al. Adevertising and aggregate consumption:an analysis of causality[J]. Econometrica, 1980, 48(5): 1149-1167.[21] Lo A W, Mackinlay A C. Data-snooping biases in tests of financial asset pricing models [J] . Review of Finance Studies, 1990, 3(3): 431-467.[22] Foser F D, Smith T, Whaley R E, et al. Assessing goodness-of-fit of asset pricing models: The distribution of the maximal R2 [J]. Journal of Fiance, 1997, 52(2): 591-607. [23] Egorov A V, Hong Y M, Li H T. Validating forecasts of the joint probability density of bond yields: Can affine models beat random walk?[J]. Journal of Econometrics, 2006, 135(1-2): 255-284.[24] Hansen P R, Lunde A, James M N. The model confidence set[J]. Econometrica, 2011, 79(2): 453-497.[25] Hansen P R, Lunde A. A test for superior predictive ability[J]. Journal of Business and Economic Statistics, 2005, 23(4): 365-380.[26] Barndorff-Nielsen O E, Shephard N. Econometric analysis of realised volatility and its use in estimating stochastic volatility models[J]. Journal of the Royal Statistical Society, 2002,64(2): 253-280.[27] Barndorff-Nielsen O E, Shephard N. Estimating quadratic variation using realized variance [J]. Journal of Applied Econometrics, 17(5): 457-478.[28] 李胜歌,张世英. 高频金融数据的两种波动率计算方法比较[J]. 系统管理学报,2007,16(4):426-431.[29] 唐勇. 金融资产跳跃检验方法实证比较[J]. 中国管理科学,2012,20:290-299.[30] 叶五一,缪柏其. 已实现波动率与日内价差条件下的CVaR估计[J]. 管理科学学报, 2012,15(8): 60-71.[31] Müller U M, Dacorogna M M, Dave R D, et al. Fractals and intrinsic time:A challenge to econometricians[C]//39th International AEA Conference on Real Time Econometrics, 1993.[32] 文凤华,刘晓群,唐海如,等. 基于LHAR-RV-V模型的中国股市波动性研究[J]. 管理科学学报,2012,15(6): 59-67.[33] 西村友作,孙便霞,门明. 全球金融危机下的股票市场波动跳跃研究:基于高频数据的中美比较分析[J]. 管理工程学报,2012,6(1):106-112.[34] 杨科,陈浪南. 基于C_TMPV的中国股市高频波动率的跳跃行为研究[J]. 管理科学,2011,24(2): 103-112.[35] 杨科,田凤平,林洪.跳跃的估计、股市波动率的预测以及预测精度评价[J]. 中国管理科学,2013,21(3):50-60.[36] 王鹏,王建琼.中国股票市场的收益分布及其SPA 检验[J].系统管理学报,2008,17(5):542-547.[37] 魏宇.金融市场的多分形波动率测度、模型及其SPA检验[J].管理科学学报,2009,12(5): 88-99. |