考虑共同跳跃的波动建模:基于高频数据视角

doi:10.16381/j.cnki.issn1003-207x.2015.08.006

中国管理科学 ›› 2015, Vol. 23 ›› Issue (8): 46-53.doi: 10.16381/j.cnki.issn1003-207x.2015.08.006

考虑共同跳跃的波动建模:基于高频数据视角

唐勇, 林欣

福州大学经济与管理学院, 福建福州 350108

收稿日期:2013-11-22 修回日期:2014-09-18 出版日期:2015-08-20 发布日期:2015-08-19
作者简介:唐勇(1970-),男(汉族),江苏淮安人,福州大学经济与管理学院教授,博士,博士生导师,研究方向:金融计量与风险管理.
基金资助:
国家自然科学基金资助项目(71171056,71473039,71101134);福建省高等学校新世纪优秀人才支持计划(2014)

Volatility Modeling in Consideration of the Co-jumps: Based on the Perspective of High-frequency Data

TANG Yong, LIN Xin

School of Economics & Management, Fuzhou University, Fuzhou 350108, China

Received:2013-11-22 Revised:2014-09-18 Online:2015-08-20 Published:2015-08-19

摘要/Abstract

摘要： 针对共同跳跃研究的不足,文章沿袭已有理论框架,采用常用的日内跳跃检验方法,构建了共同跳跃(协)方差和连续样本路径(协)方差,并扩展HAR-RV-CJ模型,将(协)方差、共同跳跃置于统一波动模型框架内。通过对上证综指和深圳成指高频数据的实证分析,结果显示两指数共同跳跃占其各自的跳跃比例较大,且基本上都是同方向的跳跃;共同跳跃(协)方差和连续样本路径(协)方差对已实现(协)方差的影响都是显著的,考虑共同跳跃影响有助于提高(协)方差建模的准确性。此研究有助于投资者优化投资策略和为监管部门提供监管基础。

关键词: 跳跃检验, 共同跳跃, 多变量, 波动率建模, 高频数据

Abstract: In consideration of the insufficience of the existing researches on co-jumps, co-jumps (co)variance and continuous sample path (co)variance are established using the common intraday jump test and the HAR-RV-CJ model is extended by taking (co)variance and co-jumps together into account, according to the existing theoretical framework. By virtue of high frequency data from Shanghai composite index and Shenzhen component index, the empirical analyses show that the number of the co-jumps of two indexes has a large proportion to their own jumps, most of the co-jumps are the same direction, co-jumps (co)variance and continuous sample path (co)variance both have significant effects on the (co)variance and considering co-jumps makes the extended model more accurate. This study makes contribution to the investment strategy optimization for investor and also provides regulatory basis for the regulatory authorities.

Key words: jump test, co-jumps, multivariate, volatility modeling, high frequency data

中图分类号:

F830

唐勇, 林欣. 考虑共同跳跃的波动建模:基于高频数据视角[J]. 中国管理科学, 2015, 23(8): 46-53.

TANG Yong, LIN Xin. Volatility Modeling in Consideration of the Co-jumps: Based on the Perspective of High-frequency Data[J]. Chinese Journal of Management Science, 2015, 23(8): 46-53.

参考文献

[1] 唐勇,张伯新.基于高频数据的中国股市跳跃特征实证分析[J].中国管理科学,2013,21(5):29-39.

[2] Barndorff-Nielsen O E, Shephard N. Measuring the impact of jumps in multivariate price processes using bipower covariation[R]. Working Paper,Nuffield College, Oxford University, 2004.

[3] Jacod J, Todorov V. Testing for common arrivals of jumps for discretely observed multidimensional processes[J]. The Annals of Statistics, 2009, 37(4): 1792-1838.

[4] Bollerslev T, Law T H, Tauchen G. Risk, jumps, and diversification[J]. Journal of Econometrics, 2008, 144(1): 234-256.

[5] Liao Yin, Anderson H M. Testing for co-jumps in high-frequency financial data: An approach based on first-high-low-last prices[R]. Working Paper Monash University, 2011.

[6] Gobbi F, Mancini C. Diffusion covartation and co-jumps in bidimensional asset price processes with stochastic volatility and infininte activityl levy jumps[R]. Working Paper, Universiy of Bologna,2008.

[7] Mancini C, Gobbi F. Identifying the Brownian covariation from the co-jumps given discrete observations[J]. Econometric Theory, 2012, 28(2): 249.

[8] Lahaye J, Laurent S, Neely C J. Jumps, cojumps and macro announcements[J]. Journal of Applied Econometrics, 2011, 26(6): 893-921.

[9] Dungey M, Hvozdyk L. Cojumping: Evidence from the US Treasury bond and futures markets[J]. Journal of Banking & Finance, 2012, 36(5): 1563-1575.

[10] Gilder D, Shackleton M B, Taylor S J. Cojumps in stock prices: Empirical evidence[J]. Journal of Banking & Finance, 2013,40(2):443-459.

[11] 欧丽莎,袁琛,李汉东.中国股票价格跳跃实证研究[J].管理科学学报,2011,14(9):60—66.

[12] Corsi F. A simple approximate long-memory model of realized volatility[J]. Journal of Financial Econometrics, 2009, 7(2): 174-196.

[13] 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.

[14] Corsi F, Renò R. Discrete-time volatility forecasting with persistent leverage effect and the link with continuous-time volatility modeling[J]. Journal of Business & Economic Statistics, 2012, 30(3): 368-380.

[15] Clements A, Liao Yin. The dynamics of co-jumps, volatility and correlation[R]. Working Paper,National Centre for Econometric Research, 2013.

[16] Andersen T G, Bollerslev T, Diebold F X, et al. Modeling and forecasting realized volatility [J]. Econometrica, 2003, 71(2): 579-625.

[17] Barndorff-Nielsen O E, Shephard N. Power and bipower variation with stochastic volatility and jumps [J]. Journal of financial econometrics, 2004, 2(1): 1-37.

[18] Bauwens L, Hafner C, Laurent B. Volatility models and their applications [M].New Jersy: Weily,2012.

[19] Andersen T G, Bollerslev T, Dobrev D. No-arbitrage semi-martingale restrictions for continuous-time volatility models subject to leverage effects, jumps and iid noise: Theory and testable distributional implications[J]. Journal of Econometrics, 2007, 138(1): 125-180.

考虑共同跳跃的波动建模:基于高频数据视角

Volatility Modeling in Consideration of the Co-jumps: Based on the Perspective of High-frequency Data

PDF (PC)

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

Metrics

本文评价

推荐阅读 0

[1]	梅韵杰,曾祥艳,闫书丽. 三参数区间灰数序列的矩阵型自回归时滞灰色多变量模型[J]. 中国管理科学, 2024, 32(9): 48-58.
[2]	梁超, 魏宇, 马锋, 李霞飞. 我国黄金期货价格波动率预测研究：来自模型缩减方法的新证据[J]. 中国管理科学, 2022, 30(4): 30-41.
[3]	李惠,曾波,周文浩. 基于灰色参数组合优化新模型的生活垃圾清运量预测研究[J]. 中国管理科学, 2022, 30(4): 96-107.
[4]	吴鑫育, 李心丹, 马超群. 基于期权与高频数据信息的VaR度量研究[J]. 中国管理科学, 2021, 29(8): 13-23.
[5]	苑慧玲, 徐路, 周勇. 带有市场交易信息和随机微观噪声下的杠杆效应研究[J]. 中国管理科学, 2020, 28(9): 12-22.
[6]	张可, 钟秋萍, 曲品品, 殷要, 左媛. 基于网络搜索信息的农村水环境质量灰色预测模型[J]. 中国管理科学, 2020, 28(6): 222-230.
[7]	瞿慧, 张壹. 基于波动择时绩效的高维波动率估计量与预测模型研究[J]. 中国管理科学, 2020, 28(5): 62-70.
[8]	马超群, 徐光鲁, 刘伟, 贾钰, 赵新伟. 询价制度改革、知情交易者概率与IPO溢价[J]. 中国管理科学, 2018, 26(8): 1-12.
[9]	刘志东, 郑雪飞. 基于Hawkes因子模型的股价共同跳跃研究[J]. 中国管理科学, 2018, 26(7): 18-31.
[10]	朱学红, 张宏伟, 钟美瑞, 刘海波. 基于高频数据的中国有色金属期货市场量价关系研究[J]. 中国管理科学, 2018, 26(6): 8-16.
[11]	罗嘉雯, 陈浪南. 多国股票市场的高频波动相关性研究[J]. 中国管理科学, 2018, 26(2): 116-125.
[12]	孔繁辉, 李健. 供应中断风险下OEM供应链弹性运作与提升策略[J]. 中国管理科学, 2018, 26(2): 152-159.
[13]	徐光鲁, 马超群, 刘伟, 贾钰. 信息披露与IPO首日回报率[J]. 中国管理科学, 2018, 26(10): 10-19.
[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[J]. 中国管理科学, 2017, 25(9): 19-27.
[15]	吴鑫育, 李心丹, 马超群. 考虑微观结构噪声的非仿射期权定价研究——基于上证50ETF期权高频数据的实证分析[J]. 中国管理科学, 2017, 25(12): 99-108.