基于GARCH-M模型的股指期货对股市波动影响的研究

doi:10.16381/j.cnki.issn1003-207x.2017.01.004

中国管理科学 ›› 2017, Vol. 25 ›› Issue (1): 27-34.doi: 10.16381/j.cnki.issn1003-207x.2017.01.004

基于GARCH-M模型的股指期货对股市波动影响的研究

曹栋^1,2, 张佳¹

1. 西安电子科技大学经济与管理学院, 陕西西安 710071;
2. 中国科学院数学与系统科学研究院, 北京 100190

收稿日期:2015-05-18 修回日期:2016-05-23 出版日期:2017-01-20 发布日期:2017-03-22
通讯作者: 曹栋(1982-),女(汉族),湖南益阳人,西安电子科技大学经济与管理学院,副教授,研究方向:宏观经济分析,金融发展、环境经济学,E-mail:ivycaodong@163.com. E-mail:ivycaodong@163.com
基金资助:
国家自然科学基金资助项目（71303181）；国家留学基金委公派高级研究学者及访问学者项目（201406965014）；教育部人文社会科学研究项目（11YJC790004）；博士后面上基金项目（2014M550864）；中央高校基金科研业务费专项资金资助项目（7214566601，7215566603）；湖南省现代服务业研究基地基金项目（16jdmszd01）

The Impact of Index Future on Stock Market Volatility Based on the GARCH-M Model

CAO Dong^1,2, ZHANG Jia¹

1. School of Economics and Management, Xidian University, Xi'an 710126, China;
2. Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China

Received:2015-05-18 Revised:2016-05-23 Online:2017-01-20 Published:2017-03-22

摘要/Abstract

摘要： 从本轮金融危机以来，伴随着沪深股市的大幅震荡，股指期货作为规避风险、价格发现、资金配置的市场工具，其稳定股市波动性的作用再次被推向了风口浪尖。在综合评价现有研究的缺陷、既有改进方法以及其应用情况后，通过在股票价格指数的生成过程中融入风险测量构建了适应我国股市的高拟合程度的GARCH-M模型，研究股指期货对股市波动的影响。本文选取2007年8月1日到2015年4月23日沪深300股票指数的日度数据，分析了我国股市在受到较大外部冲击后，股指期货的稳定作用以及股票市场的正反馈效应等。实证结果表明：我国股票市场波动表现出正反馈效应，股指期货推出前后表现出不同程度的波动性，并且呈现出明显的“波动集群性”；沪深300股指期货推出后股指的波动平均减小了4.45×10^-6个单位，已经初步发挥了股票市场的稳定器功能，一定程度上缓解了股市波动，但是其作用有限，功能还未能完全发挥；市场波动受旧信息的影响远大于新信息产生的影响，表明我国股市波动主要来源于前者，同时条件方差所受的冲击是持久的，即冲击对未来的股票指数走势都有重要作用。

关键词: 沪深300股指期货, 股市波动, GARCH-M模型

Abstract: Since the financial crisis from 2007, the Chinese stock market has experienced large volatilities. CSI300 future has received a great deal of attention in recent years for its market stable function, even though it's made to be as a hedge against the risk, price discovery, and market capital allocation tool. After analyzing shortages of existing researches, methods and their applications, the integration of risk measurement process is introduced into the GARCH-M model to measure the volatility of stock market and study the effect of stock index futures on stock market fluctuations. By using the daily data of CSI 300 from August 1st, 2007 to April 23rd, 2015, a highly fitting GARCH-M model is built. The empirical study shows the impact of CSI 300 index future on the stock market volatility after the large external shock. First, there is a positive feedback in the stock market, with different levels of volatility before and after the introduction of stock future and exhibits significant "volatility clustering". Second, the CSI 300 index future has reduced the volatility index of the average 4.45×10^-6 units, played a function as a controller, and eased the stock market volatility to some extent. However, its impact is limited and not fully realized. Finally, the market volatility in China is mainly affected by the old information far more than the new information, while its impact on the conditional variance is durable and has an more important role for the future impact of stock index movements.

Key words: CSI300 futures, stock market volatility, GARCH-M model

中图分类号:

F830

曹栋, 张佳. 基于GARCH-M模型的股指期货对股市波动影响的研究[J]. 中国管理科学, 2017, 25(1): 27-34.

CAO Dong, ZHANG Jia. The Impact of Index Future on Stock Market Volatility Based on the GARCH-M Model[J]. Chinese Journal of Management Science, 2017, 25(1): 27-34.

参考文献

[1] Cervello-Royo R, Guijarro F, Michniuk K. Stock market trading rule based on pattern recognition and technical analysis:Forecasting the DJIA index with intraday data[J]. Expert Systems with Applications, 2015, 42(14):5963-5975.

[2] Kittiakarasakun J, Tse Y, Wang G H K. The impact of trades by traders on asymmetric volatility for Nasdaq-100 index futures[J]. Managerial Finance, 2012, 38(8):752-767.

[3] Christain B E, Claudia C. Risk management with high-dimensional vine copulas:An analysis of the Euro Stoxx 50[J]. Statistics and Risk Modeling, 2013, 30(4):307-342.

[4] Frijins B, Tse Y. The informativeness of trades and quotes in the FTSE 100 index futures market[J]. Journal of futures markets, 2015, 35(2):105-126.

[5] Deville L, Gresse C, de Séverac B. Direct and indirect effects of index ETFs on spot-futures pricing and liquidity:Evidence from the CAC 40 index[J]. European Financial Management, 2014, 20(2):352-373.

[6] Wagner H, Matanovic E. Volatility impact of stock index futures trading-a revised analysis[J]. Journal of Applied Finance and Banking, 2012, 2(5):113-126.

[7] Lee Y H. What jump effects are implicit in Nikkei 225 returns and the changes in the volatility index Japan?[J]. Investment Analysts Journal, 2014, 43(80):71-78.

[8] Fung J W K, Lau F, Tse Y. The impact of sampling frequency on intraday correlation and lead-lag relationships between index futures and individual stocks[J]. Journal of Futures Markets, 2015, 35(10):1-14.

[9] 侯利强,杨善林,王晓佳,等.上证综指的股指波动-基于模糊FEGARCH模型以及不同分布假设的预测研究[J]. 中国管理科学,2015,23(6):32-40.

[10] 唐振鹏,周熙雯,黄友珀,等.基于小波方法的中国股市与亚太股市联动性实证研究[J].中国管理科学,2015,23(s):398-404.

[11] Chen Zhijuan, Lin Hai, Ma Changfeng. On the reverse U-shaped intraday pattern of volume and volatility:Evidence from CSI 300 index futures market[J/OL]. Available at SSRN:http://ssrn.com/abstract=2386457 or http://dx.doi.org/10.2139/ssrn.2386457.

[12] He Xiaoli, Wang Hongwu, Du Ziping. The complexity and fractal structures of CSI300 before and after the introduction of CSI300IF[J]. Physica A:Statistical Mechanics and Its Applications, 2014, 414:76-85.

[13] 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.

[14] Yang Jian, Yang Zihui, Zhou Yinggang. Intraday price discovery and volatility transmission in stock index and stock index futures markets:Evidence from China[J]. Journal of futures markets, 2012, 32(2):99-121.

[15] Zhou Pu, Lu Fengbin, Wang Shouyang. Testing linear and nonlinear granger causality in CSI300 futures and spot markets based on new concepts of nonlinear positive/negative spillover[J]. Journal of Systems Science and Complexity, 2014, 27(4):729-742.

[16] Guo Biao, Han Qian, Liu Maonan, et al. A tale of two index futures:The intraday price discovery and volatility transmission processes between the China financial futures exchange and the Singapore exchange[J]. Emerging Markets Finance and Trade, 2013, 49(S4):197-212.

[17] 简志宏,曾裕峰,刘曦腾.基于CAViaR型的沪深300股指期货隔夜风险研究[J].中国管理科学.2016, 24(9):1-10.

[18] 周仁才. 基于风险分解的股指期货套期保值策略研究[J].中国管理科学,2013, 21(2):17-23.

[19] 代军,朱新玲.沪深300股指期货对冲效率研究[J].中国管理科学.2014, 22(4):1-8.

[20] Bollerslev T. Generalized autoregressive conditional heteroscedasticity[J]. Journal of Econometrics, 1986, 31(3):307-327.

[21] 罗万纯,刘锐. 中国粮食价格波动分析:基于ARCH类模型[J]. 中国农村经济, 2010, (4):30-47.

[22] 周舟,董坤,汪寿阳. 基于欧洲主权债务危机背景下的金融传染分析[J]. 管理评论, 2012, 24(2):3-11.

[23] Robert E, Lilien D M, Robins R P. Estimating time varying risk premia in the term structure:The ARCH-M model[J]. Econometrica, 1987, 55(2):391-406.

基于GARCH-M模型的股指期货对股市波动影响的研究

The Impact of Index Future on Stock Market Volatility Based on the GARCH-M Model

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