李红权, 张馨心
李红权, 张馨心. 引入测量误差和马尔科夫转换机制的高频波动率建模与预测[J]. 系统科学与数学, 2022, 42(5): 1200-1215.
LI Hongquan, ZHANG Xinxin. The Forecasting Performance of the High-Frequency Volatility Models Based on Errors and Markov Regime-Switching[J]. Journal of Systems Science and Mathematical Sciences, 2022, 42(5): 1200-1215.
LI Hongquan, ZHANG Xinxin
MR(2010)主题分类:
分享此文:
[1] Engle R F. Autoregressive conditional heteroskedasticity with estimates of the variance of the United Kingdom inflation. Econometrica, 1982, 50:987-1007. [2] Bollerslev T. Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 1986, 31(3):307-327. [3] Taylor S J. Modeling Financial Time Series. Chichester:John Wiley&Sons, 1986. [4] Andersen T G, Bollerslev T. Answering the skeptics:Yes, standard volatility models do provide accurate forecasts. International Economic Review, 1998, 39(4):885-905. [5] Corsi F. A simple approximate long-memory model of realized volatility. Journal of Financial Econometrics, 2009, 7(2):174-196. [6] Andersen T G, Bollerslev T, Diebold F X. Roughing it up:Including jump components in the measurement, modeling, and forecasting of return volatility. The Review of Economics and Statistics, 2007, 89(4):701-720. [7] Barndorff-Nielsen O E, Kinnebrock S, Shephard N. Measuring Downside Risk-Realised Semivariance. New York:Oxford University Press, 2010. [8] Patton A J, Sheppard K. Good volatility, bad volatility:Signed jumps and the persistence of volatility. Review of Economics and Statistics, 2015, 97(3):683-697. [9] 刘威仪,江含宇,张天玮,等.基于高频极值数据的波动率建模与预测.系统工程理论与实践, 2020, 40(12):3095-3111.(Liu W Y, Jiang H Y, Zhang T W, et al. Volatility modeling and forecasting based on high frequency extreme value data. Systems Engineering-Theory&Practice, 2020, 40(12):3095-3111.) [10] 龚旭,曹杰,文凤华,等.基于杠杆效应和结构突变的HAR族模型及其对股市波动率的预测研究.系统工程理论与实践, 2020,40(5):1113-1133.(Gong X, Cao J, Wen F H, et al. The HAR-type models with leverage and structural breaks and their applications to the volatility forecasting of stock market. Systems Engineering-Theory&Practice, 2020, 40(5):1113-1133.) [11] 马锋,魏宇,黄登仕,等.基于马尔科夫状态转换和跳跃的高频波动率模型预测.系统工程, 34(1):10-16.(Ma F, Wei Y, Huang D S, et al. The forecasting performance of the high-frequency volatilitymodels with the Markov-switching regime and jump. Systems Engineering, 2016, 34(1):10-16.) [12] Bollerslev T, Patton A J, Quaedvlieg R. Exploiting the errors:A simple approach for improved volatility forecasting. Journal of Econometrics, 2016, 192(1):1-18. [13] 刘光强.我国股票市场高频波动预测研究——基于ARQ及HARQ模型的实证分析.西南交通大学学报(社会科学版), 2017, 18(4):82-87.(Liu G Q. The study of high frequency fluctuations forecasting for Chinese stock market:The empirical research with ARQ and HARQ Modes. Journal of Southwest Jiaotong Universitity (Social Sciences), 2017, 18(4):82-87.) [14] 宋亚琼,王新军.基于动态估计误差的中国股市波动率建模与预测.中国管理科学, 2017, 25(9):19-27.(Song Y Q, Wang X J. Modeling and forecasting volatility of Chinese stock market based on dynamic estimation errors. Chinese Journal of Management Science, 2017, 25(9):19-27.) [15] 陈声利,李一军,关涛.基于四次幂差修正HAR模型的股指期货波动率预测.中国管理科学, 2018, 26(1):57-71.(Chen S L, Li Y J, Guan T. Forecasting realized volatility of Chinese Stock Index Futures based on approved HAR models with median realized quarticity. Chinese Journal of Management Science, 2018, 26(1):57-71.) [16] 赵华,肖佳文.考虑微观结构噪声与测量误差的波动率预测.中国管理科学, 2020,28(4):48-60.(Zhao H, Xiao J W. Volatility forecasting in the rresence of microstructure noise and measurement error. Chinese Journal of Management Science, 2020, 28(4):48-60.) [17] Ma F, Wahab M I M, Huang D S, et al. Forecasting the realized volatility of the oil futures market:A regime switching approach. Energy Economics, 2017, 67:136-145. [18] Duan Y Y, Chen W, Zeng Q, et al. Leverage effect, economic policy uncertainty and realized volatility with regime switching. Physica A-Statistical Mechanics and Its Applications, 2018, 493:148-154. [19] Yu M, Song J. Volatility forecasting:Global economic policy uncertainty and regime switching. Physica A:Statistical Mechanics and Its Applications, 2018, 511:316-323. [20] Xu W, Wang J, Ma F, et al. Forecast the realized range-based volatility:The role of investor sentiment and regime switching. Physica A:Statistical Mechanics and Its Applications, 2019, 527:121422. [21] Aha A, Cyh B, Iwm A. Modelling the volatility of TOCOM energy futures:A regime switching realised volatility approach. Energy Economics, 2019, 104434, DOI:10.1016/j.eneco.2019.06.019. [22] Barndorff-Nielsen O E, Shephard N. Estimating quadratic variation using realized variance. Journal of Applied Econometrics, 2002, 17(5):457-477. [23] Hamilton J D, Susmel R. Autoregressive conditional heteroskedasticity and changes in regime. Journal of Econometrics, 1994, 64(1/2):307-333. [24] Kim C J. Dynamic linear models with Markov-switching. Journal of Econometrics, 1994, 60(1/2):1-22. [25] 魏宇.沪深300股指期货的波动率预测模型研究.管理科学学报, 2010,13(2):66-76.(Wei Y. Volatility forecasting models for CSI300 index futures. Journal of Management Sciences in China, 2010, 13(2):66-76.) [26] Hansen P R, Lunde A, James M N. The model confidence set. Econometrica, 2011, 79(2):453-497. |
[1] | 吴鑫育, 刘天宇. 具有时变波动率持续性的已实现EGARCH模型及其实证研究[J]. 系统科学与数学, 2021, 41(9): 2444-2459. |
[2] | 范国良, 饶诗文, 王江峰. 缺失数据下变系数部分非线性测量误差模型的经验似然估计[J]. 系统科学与数学, 2021, 41(9): 2643-2659. |
[3] | 赵伟, 王钟梅, 吴纯杰. 结合测量误差的检测多元协方差矩阵的EWMA控制图[J]. 系统科学与数学, 2021, 41(7): 2018-2034. |
[4] | 蔡光辉, 吴志敏. 基于门槛效应的Realized MAT-HAR GARCH模型波动预测[J]. 系统科学与数学, 2021, 41(6): 1548-1571. |
[5] | 白娟娟, 师荣蓉. 基于广义已实现测度的中国股市波动预测与 VaR 度量[J]. 系统科学与数学, 2021, 41(3): 653-666. |
[6] | 孙会霞,倪宣明,钱龙,赵慧敏. 基于长期CVaR约束的高频投资组合优化[J]. 系统科学与数学, 2021, 41(2): 344-360. |
[7] | 牛娟,谢田发,郭媛媛,孙志华. 协变量有测量误差时Tobit回归模型的估计[J]. 系统科学与数学, 2020, 40(9): 1672-1686. |
[8] | 蔡光辉,应雪海. 基于跳跃、好坏波动率和马尔科夫状态转换的高频波动率模型预测[J]. 系统科学与数学, 2020, 40(3): 521-546. |
[9] | 唐勇,崔金鑫. 中国股票市场最优套期保值比率研究------基于高阶矩HAR模型[J]. 系统科学与数学, 2018, 38(9): 1036-1054. |
[10] | 季琳琳,廖军,宗先鹏. 异方差线性测量误差模型的平均估计[J]. 系统科学与数学, 2018, 38(6): 688-701. |
[11] | 贺志芳,杨鑫,龚旭,文凤华. 股指期货市场波动率的预测研究[J]. 系统科学与数学, 2016, 36(8): 1160-1174. |
[12] | 史建红,宋卫星. 测量误差为Laplace分布的非线性统计推断[J]. 系统科学与数学, 2015, 35(12): 1510-1528. |
[13] | 马家丽,胡雪梅. 半参数可加测量误差模型的白噪声检验[J]. 系统科学与数学, 2014, 34(8): 992-1002. |
[14] | 杨徐佳,魏传华,齐飞. 约束线性测量误差模型的统计推断[J]. 系统科学与数学, 2013, 33(2): 171-178. |
[15] | 王海鹰,邹国华. 线性测量误差模型的平均估计[J]. 系统科学与数学, 2012, 32(1): 1-14. |
阅读次数 | ||||||
全文 |
|
|||||
摘要 |
|
|||||