多国股票市场的高频波动相关性研究

doi:10.16381/j.cnki.issn1003-207x.2018.02.013

中国管理科学 ›› 2018, Vol. 26 ›› Issue (2): 116-125.doi: 10.16381/j.cnki.issn1003-207x.2018.02.013

多国股票市场的高频波动相关性研究

罗嘉雯¹, 陈浪南²

1. 华南理工大学工商管理学院, 广东广州 510640;
2. 中山大学岭南学院, 广东广州 510275

收稿日期:2016-09-27 修回日期:2017-05-08 出版日期:2018-02-20 发布日期:2018-04-20
通讯作者: 陈浪南(1958-),男(汉族),福建福州人,中山大学岭南学院博士,教授,研究方向:金融经济学、货币金融学,E-mail:Llnscln@mail:sysu.edu.cn. E-mail:Llnscln@mail:sysu.edu.cn
基金资助:
教育部人文社会科学研究基金资助项目（17YJC630099，17YJA790011）；广东省自然科学基金项目（2017A0 30310391，2017A030311038）；中国博士后科学基金面上资助项目（2017M612674）；广东省社科规划课题（GD17TW01-3）

The Volatility Co-movement of Various Stock Markets based on High-frequency Data

LUO Jia-wen¹, CHEN Lang-nan²

1. School of Business Administration, South China University of Technology, Guangzhou 510640, China);
2. Lingnan College, Sun Yat-sen University, Guangzhou 510275, China

Received:2016-09-27 Revised:2017-05-08 Online:2018-02-20 Published:2018-04-20

摘要/Abstract

摘要： 本文通过建立包含马尔科夫机制转换结构的MS-MHAR-DCC模型，并选取世界上比较发达的国家和地区股票市场的高频日内交易数据为样本，对多个股票市场波动相关性进行研究。通过引入包含马尔科夫结构的外部随机矩阵，本文识别出金融市场波动相关的截断时期，正态分布设定下相比在t分布设定下识别的截断时期更多且持续时间更长。在模型的截断时期内，多个股票市场的波动相关结构主要受到正向冲击，即在截断时期内的波动相关性大于平常状态的波动相关性。本文还发现，相同地域的股票市场间的动态波动相关性在大部分时期内表现为较强的正相关；美国股票市场和其余5个国家股票市场波动的动态相关性在大部分时期都表现为较强的正相关，表明美国作为全球巨头在世界金融市场波动的引导作用。

Abstract: The financial globalization facilitates the flow of capital and good across the countries, and thus improves the operational efficiency of the financial markets. At the same time, the financial globalization enhances the risk transmission and thus leads to high volatility of financial markets.Therefore, the co-movement of financial markets is an important issue for risk managements, asset pricing and portfolio management.
A MHAR-DCC model with a Markov regime switching structure is developed to investigate the volatility co-movement of stock markets by employingthe high-frequency data from the six major stock markets in the world from 1 January 2010 to 31 December 2013.
The structural changes in co-volatility are identifed by introducing an exogenous stochastic component that follows a Markov regime switching process. There are more and longer-duration break periods identified by the model with the normal distribution as compared to that with the student-t distribution. In addition, the positive impact that scales up the covariance dominates the breakdown periods.There exhibit significantly positive correlations between the stock market within the same geographyregions as well as significantly positive correlations between the US stock market and the other markets, indicating the leading position of the US stock market in the co-movement of world stock markets.

Key words: volatility co-movement, MHAR-DCC model, high frequency data, multiple stock markets

中图分类号:

F833-5

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.

LUO Jia-wen, CHEN Lang-nan. The Volatility Co-movement of Various Stock Markets based on High-frequency Data[J]. Chinese Journal of Management Science, 2018, 26(2): 116-125.

参考文献

[1] Bollerslev T. Modelling the coherence in short-run nominal exchange rates:A multivariate generalized arch model[J].Review of Economics & Statistics, 1990,72(3):498-505.

[2] Engle R F, Kroner K F. Multivariate simultaneous generalized ARCH[J]. Econometric theory,1995,11(01):122-150.

[3] Engle R. Dynamic conditional correlation:A simple class of multivariate generalized autoregressive conditional heteroskedasticity models[J].Journal of Business & Economic Statistics, 2002, 20(3):339-350.

[4] Bauwens L, Laurent S, Rombouts J V. Multivariate GARCH models:A survey[J]. Journal of Applied Econometrics,2006, 21(1):79-109.

[5] Bekaert G, Hodrick R J, Zhang, Xiaoyan. International stock return comovements[J]. The Journal of Finance,64(6):2591-2626.

[6] Beine M, Cosma A, Vermeulen R. The dark side of global integration:Increasing tail dependence[J]. Journal of Banking & Finance, 2010, 34(1):184-192.

[7] Rapach D E, Strauss J K, Zhou G. International stock return predictability:What is the role of the United States?[J]. Journal of Finance, 2013, 68(4):1633-1662.

[8] Bubák V, Ko?enda E, ikeš, F. Volatility transmission in emerging European foreign exchange markets[J]. Journal of Banking & Finance, 2011, 35(11), 2829-2841.

[9] Sou?ek M, Todorova N. Realized volatility transmission between crude oil and equity futures markets:A multivariate HAR approach[J]. Energy Economics, 2013, 40:586-597.

[10] 刘晓星, 段斌, 谢福座. 股票市场风险溢出效应研究:基于EVT-Copula-CoVaR模型的分析[J]. 世界经济, 2011,(11):145-159.

[11] 熊正德, 文慧, 凌语蓉. 基于时频分析的农产品期货市场与外汇市场联动关系研究[J].中国管理科学, 2013,21(S1):255-263.

[12] 熊正德, 文慧, 熊一鹏. 我国外汇市场与股票市场间波动溢出效应实证研究——基于小波多分辨的多元BEKK-GARCH(1,1)模型分析[J].中国管理科学, 2015, 23(4):30-38.

[13] 王璐. 国际多元化下多维金融市场相关结构测度——以金砖国家新兴市场为对象[J]. 数理统计与管理, 2015,(3):498-512.

[14] 王鹏,魏宇,王建琼. 不同矩属性波动模型对中国股市波动率的预测精度分析[J].数理统计与管理,2010, 29(3):550-559.

[15] 何德旭, 苗文龙. 国际金融市场波动溢出效应与动态相关性[J].数量经济技术经济研究, 2015,(11):23-40.

[16] 夏南新. 国际金融市场波动非线性因果性和溢出效应[J].管理科学学报, 2016, 19(3):64-76.

[17] Jin Xin, Maheu J M. Modeling covariance breakdowns in multivariate GARCH[J]. Journal of Econometrics, 2016,194(1):1-23.

[18] Vrugt J A, TerBraak C J F, Diks C G H, et al. Accelerating Markov chain Monte Carlo simulation by differential evolution with self-adaptive randomized subspace sampling[J].International Journal of Nonlinear Sciences and Numerical Simulation, 2009, 10(3):273-290.

[19] Ahoniemi K, Lanne M. Overnight stock returns and realized volatility[J]. International Journal of Forecasting, 2005, 29(4):592-604.

[20] Koopman S J, 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.

[21] Martens M, Poon S H. Returns synchronization and daily correlation dynamics between international stock markets[J]. Journal of Banking & Finance, 2001, 25(10):1805-1827.

[22] Barndorff-Nielsen O E, Hansen P R, Lunde A,et al. Realized kernels in practice:trades and quotes[J].The Econometrics Journal, 2009, 12(3):C1-C32.

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

[24] Gelman A, Rubin D B. Inference from iterative simulation using multiple sequences[J]. Statistical Science, 1992, 7(4):457-472.

多国股票市场的高频波动相关性研究

The Volatility Co-movement of Various Stock Markets based on High-frequency Data

PDF (PC)

可视化

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 10

[1]	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.
[2]	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.
[3]	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.
[4]	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.
[5]	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.
[6]	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.
[7]	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.
[8]	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.
[9]	刘志东, 严冠. 基于半鞅过程的中国股市随机波动、跳跃和微观结构噪声统计特征研究[J]. 中国管理科学, 2016, 24(5): 18-30.
[10]	刘睿智, 周勇. 期现货市场订单流动性层面的“遛狗效应”——基于交易量刻度的高频交易数据研究[J]. 中国管理科学, 2016, 24(4): 19-26.
[11]	刘志东, 杨竞一. 基于非参数日内跳跃检验和高频数据的公司信息披露对股市价格波动影响研究[J]. 中国管理科学, 2016, 24(10): 22-34.
[12]	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.
[13]	赵军力, 梁循. 基于TrTS取样的股票收益率RV测度的改进[J]. 中国管理科学, 2015, 23(7): 26-34.
[14]	赵树然, 姜亚萍, 任培民. 高频波动率矩阵估计的比较分析——基于有噪非同步的金融数据[J]. 中国管理科学, 2015, 23(10): 19-29.
[15]	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.