Analysis of the Spillover Effect of China’s Stock Market Sectors from the Perspective of Higher Moment

TANG Yong; LI Yong-jie; ZHU Peng-fei

doi:10.14071/j.1008-8105(2019)-4023

Journal of University of Electronic Science and Technology of China(SOCIAL SCIENCES EDITION) > 2020 > 22(5): 77-89. > DOI: 10.14071/j.1008-8105(2019)-4023

TANG Yong, LI Yong-jie, ZHU Peng-fei. Analysis of the Spillover Effect of China’s Stock Market Sectors from the Perspective of Higher Moment[J]. Journal of University of Electronic Science and Technology of China(SOCIAL SCIENCES EDITION), 2020, 22(5): 77-89. DOI: 10.14071/j.1008-8105(2019)-4023

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Analysis of the Spillover Effect of China’s Stock Market Sectors from the Perspective of Higher Moment

1.
Fuzhou University　Fuzhou　350116　China
2.
Putian University　Putian　351100　China
3.
Fujian Provincial Key Laboratory of Fintech Innovation　Fuzhou　350116　China

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Received Date: January 05, 2020
Available Online: March 01, 2020

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Abstract

[Purpose/Significance] In the event of great market volatility, despite similar trends shown by different sector indexes, they still demonstrate different characteristics of volatility and spillover in their ups and downs. [Design/Methodology] This paper adopts GARCHSK model to describe the volatility characteristics of different moments of various sectors in China’s stock market. Furthermore, this paper estimates the magnitude and direction of the first moment, second moment and higher moment risk spillover effects of sectors in China’s stock market between 2004 and 2018. [Findings/Conclusions] Results show that all sector indexes manifest significant characteristics of higher moment volatility, and strong risk linkage effect of each moment is more likely to spread the risk from one single sector to the whole market due to the interaction between sectors. When crisis breaks out, the volatility spillover indexes could reflect the market changes in a timely manner compared to the spillover indexes of returns, and the higher moment volatility spillover indexes can accurately reflect the features of and the shock to different sectors.
- higher moment,
- industry effect,
- risk spillover,
- spillover indexes

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FORBES K J, RIGOBON R. No Contagion, Only Interdependence: Measuring Stock Market Comovements[J]. The Journal of Finance, 2002, 57(5): 39.

Xiao-Ming Li, Li-Ping Zou. How do Policy and Information Shocks Impact Co-movements of China’s T-bond and Stock Markets?[J]. Journal of Banking and Finance, 2007, 32(3): 0-359.

徐飞, 唐建新, 程利敏. 国际贸易网络与股价崩盘传染:竞争性货币贬值视角[J]. 国际金融研究, 2018(12): 84-93.

万蕤叶, 陆静. 金融危机期间汇率风险传染研究[J]. 管理科学学报, 2018, 21(6): 12-28.

CHEN Q, WENG X. Information Flows Between the US and China’s Agricultural Commodity Futures Markets – Based on VAR-BEKK-Skew-t Model[J]. Emerging Markets Finance and Trade, 2017, 54(1-3): 71-87.

WANG G J, XIE C, JIANG Z Q, et al. Extreme Risk Spillover Effects in World Gold Markets and the Global Financial Crisis[J]. International Review of Economics & Finance, 2016, 46(11): 55-77.

叶五一, 张浩, 缪柏其. 石油和汇率间风险溢出效应分析——基于MV-CAViaR模型[J]. 系统工程学报, 2018, 33(1): 55-64.

LI X, WEI Y. The Dependence and Risk Spillover Between Crude Oil Market and China Stock Market: New Evidence from a Variational Mode Decomposition-based Copula Method[J]. Energy Economics, 2018, 74(8): 565-581.

王朝阳, 陈宇峰, 金曦. 国际油价对中国新能源市场的传导效应研究[J]. 数量经济技术经济研究, 2018, 35(4): 131-146.

CHIANG T C, LAO L, XUE Q. Comovements Between Chinese and Global Stock Markets: Evidence from Aggregate and Sectoral Data[J]. Review of Quantitative Finance and Accounting, 2016, 47(4): 1003-1042. doi: 10.1007/s11156-015-0529-x

MENSI W, HAMMOUDEH S, NGUYEN D K, et al. Global Financial Crisis and Spillover Effects Among the U.S. and BRICS Stock Markets[J]. International Review of Economics & Finance, 2016, 42(3): 257-276.

BERNA K U, OMID S. The Interactions Between OPEC Oil Price and Sectoral Stock Returns: Evidence from China[J]. Physica A: Statistical Mechanics and its Applications, 2018, 508: 631-641. doi: 10.1016/j.physa.2018.02.185

陈暮紫, 陈敏, 吴武清, 等. 中国A股市场行业板块间领滞关系的动态变化实证研究[J]. 系统工程理论与实践, 2009, 29(6): 19-31.

苏民. 中国A股行业溢出效应的实证分析——基于小波分解和多元GARCH-VAR模型[J]. 系统工程, 2017, 35(12): 15-24.

黄乃静, 张冰洁, 郭冬梅, 等. 中国股票市场行业间金融传染检验和风险防范[J]. 管理科学学报, 2017, 20(12): 19-28+86.

叶五一, 谭轲祺, 缪柏其. 基于动态因子Copula模型的行业间系统性风险分析[J]. 中国管理科学, 2018, 26(3): 1-12.

刘向丽, 顾舒婷. 房地产对金融体系风险溢出效应研究——基于AR-GARCH-CoVaR方法[J]. 系统工程理论与实践, 2014, 34(S1): 106-111.

胡利琴, 胡蝶, 彭红枫. 机构关联、网络结构与银行业系统性风险传染——基于VAR-NETWORK模型的实证分析[J]. 国际金融研究, 2018(6): 53-64.

傅强, 张颖. 我国金融系统的风险溢出效应研究——基于溢出指数的实证分析[J]. 宏观经济研究, 2015(7): 45-51+117.

陈建青, 王擎, 许韶辉. 金融行业间的系统性金融风险溢出效应研究[J]. 数量经济技术经济研究, 2015, 32(9): 89-100.

严伟祥, 张维, 牛华伟. 金融风险动态相关与风险溢出异质性研究[J]. 财贸经济, 2017, 38(10): 67-81.

曾裕峰, 简志宏, 彭伟. 中国金融业不同板块间风险传导的非对称性研究—基于非对称MVMQ-CAViaR模型的实证分析[J]. 中国管理科学, 2017, 25(8): 58-67.

杨子晖, 陈雨恬, 谢锐楷. 我国金融机构系统性金融风险度量与跨部门风险溢出效应研究[J]. 金融研究, 2018(10): 19-37.

BOLLERSLEV T, BOLLESLEV T. Generalized Autoregressive Conditional Heteroskedasticity[J]. Eeri Research Paper, 1986, 31(3): 307-327.

MABROUK S, SAADI S. Parametric Value-at-Risk Analysis: Evidence from Stock Indices[J]. Quarterly Review of Economics & Finance, 2012, 52(3): 305-321.

WEIß, G N. F. Copula-GARCH Versus Dynamic Conditional Correlation: an Empirical Study on VaR and ES Forecasting Accuracy[J]. Review of Quantitative Finance & Accounting, 2013, 41(2): 179-202.

SU J B, LEE M C, CHIU C L. Why does Skewness and the Fat-tail Effect Influence Value-at-risk Estimates? Evidence from alternative capital markets[J]. International Review of Economics & Finance, 2014, 31: 59-85.

NG E V K. Measuring and Testing the Impact of News on Volatility[J]. The Journal of Finance, 1993, 48(5): 1749-1778. doi: 10.1111/j.1540-6261.1993.tb05127.x

SENGUPTA J K, ZHENG Y. Estimating Skewness Persistence in Market Returns[J]. Applied Financial Economics, 1997, 7(5): 549-558. doi: 10.1080/096031097333411

SIDDIQUE H A. Conditional Skewness in Asset Pricing Tests[J]. The Journal of Finance, 2000, 55(3): 1263-1295. doi: 10.1111/0022-1082.00247

HARVEY C R, SIDDIQUE A. Autoregressive Conditional Skewness[J]. Journal of Financial & Quantitative Analysis, 1999, 34(4): 465-487.

BROOKS C, BURKE S P, HERAVI S, et al. Autoregressive Conditional Kurtosis[J]. Journal of Financial Econometrics, 2005, 3(3): 399-421. doi: 10.1093/jjfinec/nbi018

Ángel León, RUBIO G, SERNA G. Autoregresive Conditional Volatility, Skewness and Kurtosis[J]. Quarterly Review of Economics & Finance, 2005, 45(4): 599-618.

MARTELLINI L, ZIEMANN V. Improved Estimates of Higher-Order Comoments and Implications for Portfolio Selection[J]. Review of Financial Studies, 2010, 23(4): 1467-1502. doi: 10.1093/rfs/hhp099

JANG J, KANG J. An Intertemporal CAPM with Higher-order Moments[J]. The North American Journal of Economics and Finance, 2017, 42(11): 314-337.

王鹏, 吴金宴. 基于协高阶矩视角的沪港股市风险传染分析[J]. 管理科学学报, 2018, 21(168(6)): 34-47.

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(9): 1036-1054.

朱鹏飞, 唐勇. 时-频域视角下的集成投资组合策略研究[J]. 系统工程理论与实践, 2020, 40(1): 13-27. doi: 10.12011/1000-6788-2018-2402-15

DIEBOLD F X, YILMAZ K. Measuring Financial Asset Return and Volatility Spillovers, with Application to Global Equity Markets[J]. Economic Journal, 2009, 119(534): 158-171. doi: 10.1111/j.1468-0297.2008.02208.x

DIEBOLD F X, YILMAZ K. Better to Give than to Receive: Predictive Directional Measurement of Volatility Spillovers[J]. International Journal of Forecasting, 2012, 28(1): 57-66. doi: 10.1016/j.ijforecast.2011.02.006

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