基于限售股解禁背景下的股票波动性研究
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摘要
波动性是衡量股市稳定性程度的一个重要指标,也是股市是否成熟有效的一个重要标志。中国股票市场自建立起步到现在已取得了长足的进步,但长期来的各项研究均显示我国的市场在大部分时间段里,都表现出强于国外成熟金融市场的波动性特征,市场有效性较低,一般只能达到弱势有效市场的程度。而相伴与中国股市诞生所建立的“股权分置”制度,就一直以来被认为是影响股票市场有效性提升的重要因素之一。中国股市自2005年9月实施股权分置改革之后,已开始进入全流通时代。改革完成后,伴随着股市中流通股比例的增加,股市中纯投机因素应有所抑制,股市的波动性应有所减弱,进而将显示出市场有效性程度提高。而限售股集中解禁作为中国股票市场的一次性事件,为检验现阶段的股市效率提供了一个“自然实验”的机会,针对这一课题所进行的研究将具有极大的现实意义。
本文首先从限售股解禁量时间变化的角度,研究了在解禁量较为集中的时间段里沪指波动的情况,并逐一分析判断各时间段中的波动是否与限售股解禁直接相关,结果显示股指的波动大多是受短期消息面因素影响。实证分析环节中,文章采用ARCH类模型对一系列已开始解禁限售股的股票进行研究,以波动性特征分析为脉络,验证限售股解禁对股价波动性的影响。实证结果表明,我国股价波动具有显著的长记忆性、非正态性、集聚效应和杠杆效应,但不存在明显的风险溢价性;数据结果前后对比来看,限售股解禁对股价的波动性虽有一定程度的抑制作用,而且短期因素对股价波动的影响变小,但这些变化还不十分明显。在实证研究的基础上,文章总结了限售股解禁前后股价波动性特征的现实表现,探讨了现阶段中国股票市场低效性的原因,并提出了具体的对策建议。
Volatility is an important indicator to measure the degree of stock market stability, and an important sign of its maturity and effective. Chinese stork market grows fast and has made great progress since it was founded. But lots of research showed that the market volatility is much larger than that of foreign mature markets for much of the time, and market efficiency is low and always be weak-form efficiency. Non-tradable share system is considered one of the important factors of low market efficiency, which was generated with the birth of Chinese stork market. Since the beginning of non-tradable share reform in September 2005, China’s stock market has entered the full circulation times. With the increase in the proportion of outstanding shares, purely speculative factors should be inhibited, and volatility should be weakened in stock market. The centralized circulating of restricted stock, which is a one-off, provides a opportunity to testing Chinese stork market current efficiency. There is great practical significance to study this issue.
Firstly, basing on the time-varying of the circulated amount, this article researched the volatility of Shanghai Stock Index, and judged that if index changes were caused by restricted stock circulation. The result is volatility always be impacted by news. In order to understanding the change of volatility in this process, Autoregressive Conditional Heteroscedasticity Model is used to study of a series of stocks’volatility character whose restricted stock have circulated, and writer tries to verify the volatility of stock is impacted by restricted stock circulation. The empirical results show that stock price volatility has significantly long memory, non-normality, accumulative effect and leverage effect, except for risk premium. By comparing the data before and after, the results showed that restricted stock circulation could weaken the volatility of stock to a certain degree, and the influences over stock price fluctuations which come from short-term factors have decreased, but these changes are not notably. Basing on the empirical study, this paper summarized the actual performance of the volatility character changes, researched the reasons of Chinese stock market inefficiency, and proposed some supporting reform measures.
本文首先从限售股解禁量时间变化的角度,研究了在解禁量较为集中的时间段里沪指波动的情况,并逐一分析判断各时间段中的波动是否与限售股解禁直接相关,结果显示股指的波动大多是受短期消息面因素影响。实证分析环节中,文章采用ARCH类模型对一系列已开始解禁限售股的股票进行研究,以波动性特征分析为脉络,验证限售股解禁对股价波动性的影响。实证结果表明,我国股价波动具有显著的长记忆性、非正态性、集聚效应和杠杆效应,但不存在明显的风险溢价性;数据结果前后对比来看,限售股解禁对股价的波动性虽有一定程度的抑制作用,而且短期因素对股价波动的影响变小,但这些变化还不十分明显。在实证研究的基础上,文章总结了限售股解禁前后股价波动性特征的现实表现,探讨了现阶段中国股票市场低效性的原因,并提出了具体的对策建议。
Volatility is an important indicator to measure the degree of stock market stability, and an important sign of its maturity and effective. Chinese stork market grows fast and has made great progress since it was founded. But lots of research showed that the market volatility is much larger than that of foreign mature markets for much of the time, and market efficiency is low and always be weak-form efficiency. Non-tradable share system is considered one of the important factors of low market efficiency, which was generated with the birth of Chinese stork market. Since the beginning of non-tradable share reform in September 2005, China’s stock market has entered the full circulation times. With the increase in the proportion of outstanding shares, purely speculative factors should be inhibited, and volatility should be weakened in stock market. The centralized circulating of restricted stock, which is a one-off, provides a opportunity to testing Chinese stork market current efficiency. There is great practical significance to study this issue.
Firstly, basing on the time-varying of the circulated amount, this article researched the volatility of Shanghai Stock Index, and judged that if index changes were caused by restricted stock circulation. The result is volatility always be impacted by news. In order to understanding the change of volatility in this process, Autoregressive Conditional Heteroscedasticity Model is used to study of a series of stocks’volatility character whose restricted stock have circulated, and writer tries to verify the volatility of stock is impacted by restricted stock circulation. The empirical results show that stock price volatility has significantly long memory, non-normality, accumulative effect and leverage effect, except for risk premium. By comparing the data before and after, the results showed that restricted stock circulation could weaken the volatility of stock to a certain degree, and the influences over stock price fluctuations which come from short-term factors have decreased, but these changes are not notably. Basing on the empirical study, this paper summarized the actual performance of the volatility character changes, researched the reasons of Chinese stock market inefficiency, and proposed some supporting reform measures.
引文
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[11] Zakaran, J. M. Threshold Heteroskedastic Models. Journal of Economic Dynamics and Control, 1994,18: 931– 944.
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[38]吴建民,庄菁.分形分布在股票市场的应用研究[J].中国流通经济,2007,(03).
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[47] Panas E. Estimating Fractal Dimension Using Stable Distributions and Exploring Long Memory through ARFIMA Models in Athens Stock Exchange[J].Applied Financial Economics, 2001,11(3):395-402.
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[2] Bollerslev, Tim. Generalized Autoregressive Conditional Heteroskedastticity [J]. Journal of Econometrics, 1986, 31:307-327.
[3] Engle,Robert, David M.Lilien, and Russell P.Robins. Estimating Time Varying Risk Premia in the Term structure:The ARCH-Model.Economertrica,1987,55:391- 406.
[4]唐齐鸣,陈健.中国股市的ARCH效应分析[J].世界经济, 2001,(03).
[5]王玉荣.中国股票市场波动性研究——ARCH模型族的应用[J].河南金融管理干部学院学报, 2002,(05).
[6]皮天雷.我国沪市波动聚集性GARCH效应的实证研究[J].管理科学, 2003,(06).
[7]李存行.沪市股票价格的波动性研究[J].统计与决策, 2005,(02).
[8]陈盛双,李化想.沪市股指收益率及波动性的实证分析[J].时代金融, 2007,(08).
[9] Engle, Robert F, and Victor K. Ng. Measuring and Testing the Impact of News on Volatility. Journal of Finance, 1993, 48: 1022 - 1082.
[10] Nelson, Daniel B. Conditional Heterosdasticity in Asset Returns: A New Apporach. Econometrica, 1991, 59: 347– 370.
[11] Zakaran, J. M. Threshold Heteroskedastic Models. Journal of Economic Dynamics and Control, 1994,18: 931– 944.
[12] Glosten, L. R. ,Jagannathan, and D. Runkle. On the Relation Between the Expected Value and the Volatility of the Nominal Excess Return on Storks. Journal of Finance, 1993,48: 1779– 1801.
[13] Koutmos, G. and G. G. Booth. Asymmetric Volatility Transmission in International Stock Markets[J]. Journal of International Money and Finance, 1995, vol 14: 747-762.
[14] Koutmos, G.. Modeling the Dynamic Interdependence of Major European Stock Markets[J]. Journal of Business Finance and Accounting, 1996, vol 23: 975 - 988.
[15]陈泽忠,杨启智,胡金泉.中国股票市场的波动性研究——EGARCH-M模型的应用[J].决策借鉴, 2000,(05).
[16]朱永安,曲春青.上海股票市场两阶段波动非对称性实证研究[J].统计与信息论坛, 2003,(04).
[17]唐齐鸣,崔筠.我国股票市场信息“杠杆效应”的实证研究[J].华中科技大学学报(社会科学版) , 2005,(02).
[18]胡永宏,陆忠华.沪深股市杠杆效应的实证分析[J].数学的实践与认识, 2005,(03).
[19]王晓芳,高继祖.沪深股市日收益率波动性比较分析[J].东南大学学报(哲学社会科学版) , 2007,(05).
[20]李小为.股票市场波动非对称性的实证研究[J].中外企业家, 2006,(08).
[21]闫涛,孙涛.上海股票市场波动的非对称性和杠杆效应研究[J].金融发展研究, 2009,(06).
[22] Mandelbrot B. The Variation of Certain Speculative Prices[J]. Journal of Business, 1963(36):394 - 419.
[23] Fama E F. The Behavior of Stock Market Prices[J].Journal of Business,1965(38):34 - 105.
[24] Peters. E. E. Chaos and Order in the Capital Markets[M]. John Wiely & Sons, Inc. New York, 1991.
[25] Paretz. P.D. The Distribution of Share price Changes[J], Journal of Business, 1972,45: 317– 335.
[26] Madan DB, Seneta E., The Variance Gamma Model for Share Market Returns [J]. Journal of Business, 1990,63: 511– 524.
[27] R Mantegna, H Stanley.Scaling, Behavior in the Dynamics of an Economic Index[J]. Nature,1995,376(6535):46-49.
[28] Mikael Linden, A Model for Stork Return Distribution[J]. International Journal of Finance and Economic, 2001,6: 159– 169.
[29]陈彬.我国证券市场收益波动度及相关性分析[J].现代财经-天津财经学院学报,2001,(11).
[30]宫汝凯.我国股市ARCH效应的实证研究[J].金融与经济,2008,(08).
[31]丁洁.上证指数收益率的波动性特征分析[J].商场现代化,2006,(14).
[32]陈盛双,李化想.沪市股指收益率及波动性的实证分析[J].时代金融,2007,(08).
[33]华雯君.基于GARCH模型族的深圳股市波动性分析[J].当代经济,2008,(09).
[34]耿浩翔. GARCH族模型在中国股市中的应用——上证综合指数波动性研究[J].企业导报,2009,(03).
[35]赵桂芹,曾振宇.股票收益的非正态分布模型[J].当代经济,2002,(10).
[36]唐林俊,杨虎.深沪股市收益率分布特征的统计分析[J].数理统计与管理,2004,(05).
[37]张慧莲,汪红驹. Scaled-t分布、杠杆效应和上证综指的VaR风险[J].南方经济,2006,(03).
[38]吴建民,庄菁.分形分布在股票市场的应用研究[J].中国流通经济,2007,(03).
[39]童泳欢.基于厚尾分布的市场风险测度[J].中山大学研究生学刊(社会科学版),2008(03).
[40] Engle and Bollerlev. Modeling the Persistence of Conditional Variances. Economitic Reviews[J]. 1986,5: 1– 50.
[41] Andrew W. Lo. Long-term Memory in Stock Market Prices[J].Journal of International Money and Finance,1991,(14).
[42] Carto N. Some International Evidence Regarding the Stochastic Behavior of Stock Returns[J]. Applied Financial Economics,1994,(4):33-39.
[43] Cheung Y, Lai K S.A search for Long Memory in International Stock Market Returns[J]. Journal of International Money and Finance,1995,14(4):597-615.
[44] Mike K.P.SO. Long-term Memory in Stock Market Volatility[J].Applied Financial Economics, 2000,(10).
[45] Bollerslev T,Mikkelsen H O. Modeling and Pricing Long Memory in Stock Market Volatility[J]. Journal of Econometrics, 1996,(06).
[46] Barkoulas J T, Baum C F, Travlos N. Long Memory in the Greek Stock Market[J].Applied Financial Economics, 2000,10(2):77-84.
[47] Panas E. Estimating Fractal Dimension Using Stable Distributions and Exploring Long Memory through ARFIMA Models in Athens Stock Exchange[J].Applied Financial Economics, 2001,11(3):395-402.
[48] Cavalcante J. Long Range Dependence in the Returns and Volatility of the Brazilian Stock Market[J]. http://www.sbe.org.br/ebe24/035.pdf.
[49]徐龙炳,陆蓉.R/S分析探索中国股票市场的非线性[J].预测,1999,(02).
[50]史永东.上海证券市场的分形结构[J].预测,2000,(05).
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