沪深股市波动性研究
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摘要
股票市场充满不确定性。信息、资本的快速流动使股票价格不断变化,从而导致市场之间互相传导。股票价格波动是股市的基本特征,正常的波动有利于活跃股票市场,使市场交易得以持续进行,但剧烈、频繁的波动就会增加市场风险,影响投资者判断,甚至打击投资者信心。我国沪深股票市场是个新兴市场,市场波动的高风险特征显得尤为突出。因此,研究我国沪深股市的波动特征对于政府管理层和投资者均具有很强的现实意义。
本文基于信息分解和EC-EGARCH-M模型对沪深股市波动性进行了实证分析。主要分六个部分展开论述,第一部分说明论文的研究背景、研究意义、研究思路及研究内容。第二部分对国内外的研究现状进行梳理。第三部分系统介绍波动的含义、类型、特征及其度量方法,为后文展开研究奠定基础。第四部分对国内外刻画市场波动的模型——ARCH族模型的特征及其应用范围进行详细分析,并思考适合沪深股市波动特征的模型。第五部分结合沪深股市的基本特征,摒弃前人简单地将成交量作为信息替代变量的方法,而构建市场信心指数和市场活跃指数作为信息的替代变量,在模型设计上,我们也考虑了条件方差对收益率的影响。在文章的第六部分,我们对模型作进一步改进,将协整分析的方法运用到建模过程中,并且考虑信息的非对称性影响,最后构建了EC-EGARCH-M模型。
文中所有模型均是利用上证综指和深证综指的日收益率数据进行的实证分析。结果表明:两种信息变量对市场波动有绝对的影响;市场活跃指数对波动有非对称的影响效果;上海市场的市场信心指数无非对称性的影响,但深圳市场低迷的市场信心会引起市场更大的波动;市场活跃度指数和协整残差项对条件均值方程和条件方差方程有很好的解释力;两市之间存在双向波动溢出,并都呈现出波动的集聚性和非对称性特征。
This paper is an empirical study on the volatility of Shanghai and Shenzhen stock markets based on the divided information and EC-EGARCH-M model.Stock market is full of uncertainty.The rapid flow of information and capital leads to frequent price changes in stock market,which in turn results in market fluctuation.Fluctuation of stock price is a normal characteristic of stock market.Normal fluctuation of stock price can promote the activity and make market transactions to be sustained. Nevertheless, dramatic and frequent fluctuation can increase risk of market,and impact investors' judgments, even seriously affect investors' confidence.Being an emerging market, China's stock market is characterized by high risk of market fluctuation. Therefore,the research of characteristics of Shanghai and Shenzhen stock markets' fluctuation has ,to administrators and investors, most practical significance.
This paper does an empirical analysis on the Shanghai and shenzhen stock markets' volatility based on the information decomposition and EC-EGARCH-M model.We start our discuss from six parts by sequence.At first,this paper introduce background, significance,thought and content of our research.Secondly,we generalize the current research results in China and abroad. In the part three, this article systematiclly introduce the meaning of volatility, type, typical characteristics and measuring methods,they lay the foundation for the next researchs.Then we analyze detaily the feature and application ranges of traditional fluctuation models—ARCH family models' features,and consider the model which fit Chinese stock markets’volatility characteristic.In the part five,according to shanghai and Shenzhen stock market' features , the paper banish the method that previous researcher take turnover simply as information variable,and constructs market confidence index and market activity index to serve as inrormation variables,about model,we consider the fact that Conditional variance can affect the rate of return.At last,we improve the model furtherly, and apply mainly cointegration analysis method to the modeling process, at the same time,and consider the asymmetric impact of information.Based on the above considerations EC-EGARCH-M model is established.
In the all models we use Shanghai Composite Index and Shenzhen Composite Index as experimental data. Results show that the two information variables have absolute effect on the market;Market activity index has a character of asymmetric effect;the market confidence index of Shanghai doesn’t have an asymmetric effect,but the sluggish condifidence of Shenzhen market will magnify volatility;market activity index and the coinergrating residuals are the two important explanatory variables for the conditional mean equation and the conditional variance equation;there are two-way volatility spillovers between Shanghai and Shenzhen stock market,besides,there are characteristic of volatility clustering and asymmetry in two markets.
本文基于信息分解和EC-EGARCH-M模型对沪深股市波动性进行了实证分析。主要分六个部分展开论述,第一部分说明论文的研究背景、研究意义、研究思路及研究内容。第二部分对国内外的研究现状进行梳理。第三部分系统介绍波动的含义、类型、特征及其度量方法,为后文展开研究奠定基础。第四部分对国内外刻画市场波动的模型——ARCH族模型的特征及其应用范围进行详细分析,并思考适合沪深股市波动特征的模型。第五部分结合沪深股市的基本特征,摒弃前人简单地将成交量作为信息替代变量的方法,而构建市场信心指数和市场活跃指数作为信息的替代变量,在模型设计上,我们也考虑了条件方差对收益率的影响。在文章的第六部分,我们对模型作进一步改进,将协整分析的方法运用到建模过程中,并且考虑信息的非对称性影响,最后构建了EC-EGARCH-M模型。
文中所有模型均是利用上证综指和深证综指的日收益率数据进行的实证分析。结果表明:两种信息变量对市场波动有绝对的影响;市场活跃指数对波动有非对称的影响效果;上海市场的市场信心指数无非对称性的影响,但深圳市场低迷的市场信心会引起市场更大的波动;市场活跃度指数和协整残差项对条件均值方程和条件方差方程有很好的解释力;两市之间存在双向波动溢出,并都呈现出波动的集聚性和非对称性特征。
This paper is an empirical study on the volatility of Shanghai and Shenzhen stock markets based on the divided information and EC-EGARCH-M model.Stock market is full of uncertainty.The rapid flow of information and capital leads to frequent price changes in stock market,which in turn results in market fluctuation.Fluctuation of stock price is a normal characteristic of stock market.Normal fluctuation of stock price can promote the activity and make market transactions to be sustained. Nevertheless, dramatic and frequent fluctuation can increase risk of market,and impact investors' judgments, even seriously affect investors' confidence.Being an emerging market, China's stock market is characterized by high risk of market fluctuation. Therefore,the research of characteristics of Shanghai and Shenzhen stock markets' fluctuation has ,to administrators and investors, most practical significance.
This paper does an empirical analysis on the Shanghai and shenzhen stock markets' volatility based on the information decomposition and EC-EGARCH-M model.We start our discuss from six parts by sequence.At first,this paper introduce background, significance,thought and content of our research.Secondly,we generalize the current research results in China and abroad. In the part three, this article systematiclly introduce the meaning of volatility, type, typical characteristics and measuring methods,they lay the foundation for the next researchs.Then we analyze detaily the feature and application ranges of traditional fluctuation models—ARCH family models' features,and consider the model which fit Chinese stock markets’volatility characteristic.In the part five,according to shanghai and Shenzhen stock market' features , the paper banish the method that previous researcher take turnover simply as information variable,and constructs market confidence index and market activity index to serve as inrormation variables,about model,we consider the fact that Conditional variance can affect the rate of return.At last,we improve the model furtherly, and apply mainly cointegration analysis method to the modeling process, at the same time,and consider the asymmetric impact of information.Based on the above considerations EC-EGARCH-M model is established.
In the all models we use Shanghai Composite Index and Shenzhen Composite Index as experimental data. Results show that the two information variables have absolute effect on the market;Market activity index has a character of asymmetric effect;the market confidence index of Shanghai doesn’t have an asymmetric effect,but the sluggish condifidence of Shenzhen market will magnify volatility;market activity index and the coinergrating residuals are the two important explanatory variables for the conditional mean equation and the conditional variance equation;there are two-way volatility spillovers between Shanghai and Shenzhen stock market,besides,there are characteristic of volatility clustering and asymmetry in two markets.
引文
[1] Benoit Mandelbrot.The Variation of Certain Speculative Prices[J].Jorrnal of Business,1963, 36(4):115-130.
[2] Engle R F.. Autoregressive Conditional Heteroskedasticity with Estimates of the VARiance of United Kingtom Inflation[J]. Econometrica,1982,50(7):203-224.
[3] R.F.Engel,D.M.Lilien,and R.P.Robins.Estimating time varing risk premia in the term structure: The ARCH-M model[J].Econometrica,1987,55:391-407.
[4] Bollerslev.T.Generalized regressive conditional heteroskedasticity[J].Journal of Econometrics, 1986,31(8):307-327.
[5] Engle,Robert F,David M.Lilien,and Russell P.Robins.Estimating time varying risk:premia in theterm structure.The ARCH-M model[J].Econometrics,1987,55(10):391-407.
[6] Glosten,L.R.,Jagan.nathan,R.Runkle,D.E.on the relation between the expected value and thevolatility of the norminal excess return on stocks[J]. Journal of Finance,1992, 48(12):1779-1801.
[7] Zakoian,J.M.Threshold Heteroskedastic model[J].Journal of Economic Dynamics and Control, 1994,18(4):931-955.
[8] Nelson,Daniel B.Conditional heteroskedasticity in Econometrics,asset returns:A new approach[J]. Econometrics,1990,59(11):347-370.
[9] Bernake,Mark.,Stock returns and volatility[J].Journal of Financial and Quantitative Analysis, 1991,25(6):203-215.
[10] Engle,Mustafa.Asset pricing with a factor ARCH covariance structure:empirical estimates for treasury bills[J].Journal of Econometrics,1992,45(10):213-238.
[11] French,Kenneth,G.William Schwert,Stambaugh.Expected stock returns and volatility[J]. Journal of Financial Economics,1987,19(5):3-30.
[12] Nelson,Daniel.B.Modeling stock market volatility change.Proceedings of the American[J]. Statistical Association Business and Economic Statistics Section,1989,7:93-98.
[13] G.William Schwert.Stock market volatility[J],Financial Analysis Journal,1990,5:23-34.
[14] Glosten L.R.,Jagannathan,D.Runkle,value and the volatility of the nominal finance,On the relationbetween the expected excess return on stocks[J]. Journal of stocks 1993, 48(11):1779-1801.
[15] Engle R.F.and Ng.V K.Measuring and testing the impact of news on volatility[J].Journal of Finance.1993,48(11);1749-1801.
[16] Crouhy and Rockinger.Volatility Clustering,Asymmetry and Hysteresis in Stock Returns: Inernational Evidence[J].Asia-Pacific Financial Markets,1997,4(11):1-35.
[17] Chiang and Doong.Emprical Analysis of Stock Returns and Volatility:Eviedence from Seven Asian Stock Markets Based on TAR-GARCH Model[J].Spinger Netherlands,2001, 17(11):301-318.
[18] Taylor S J.Modeling financial time series[J].Econoetria,1986,56(4):201-220.
[19] A Harvey,E Ruiz,N Shephard.Multivariate stochastic variance models[J].The Review of Economic,1994,61(2) :247-264.
[20] A Melino,SM Turnbull.Pricing foreign currency options with stochastic volatility[J].Journal of Econometrics,1990,45(2):239-265.
[21] D Duffle.Simulated moments estimation of Markov Modelsof asset prices[J]. Journal of Econometrics ,1993,61(4):929-952.
[22] KJ Singleton.Estimation of affine asset pricing models using the empirical characteristic function[J].Journal of Econometrics,2001,102(1):111-141.
[23] J.Danielsson and J.F.Richard.Accelerated Gaussian Importance Sampler With Application to Dynamic Latent Variable Models[J].Journal of Applied Econometrics,1993,8(12):153-173.
[24] J.Danielsson.Stochastic volatility in asset prices:Estimation with simulated maximum likelihood[J].Journal of econometrics,1994,64(2):375-400.
[25]陆金海.中国货币需求函数长期均衡实证分析[J].厦门大学学报, 2000,(1):32-36.
[26]吴长风.利用回归GARCH模型对我国沪深股市的分析[J].预测,1999,7(2):46-47.
[27]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,2000,16(5):24-27.
[28]孙培源,施东辉.跌幅限制降低了股价波动吗?——来自中国股票市场的证据[J].证券市场导报,2001,11:30-36.
[29]吴冲锋,刘海龙,吴文锋,穆启国.涨跌幅限制与股价变动研究.上证联合研究计划第三期课题,2001.
[30]许均华,李启亚.宏观政策对我国股市影响的实证研究[J].经济研究2001,9:38-45.
[31]陈浪南,黄杰鳗,中国股票市场波动非对称性的实证研究[J].金融研究,2002,(7):75-77.
[32]胡海鹏,方兆本.用AR-EGARCH-M模型对中国股市波动性的拟合分析[J].系统工程.2002, 18(7):32-36.
[33]李亚静,何跃,朱宏泉.中国股市收益率与波动长记忆性的实证研究[J].系统工程理论与实践2003(1):9-14.
[34]丁华.股价指数波动中的ARCH现象.数量经济与技术经济研究[J].2003,12(9):22-25.
[35] Peker K., Clark. A Subordinated Stochastic Process Model with Finite Variance ForSpeculative Prices[J]. Econometrica,1973,41:77-85.
[36] Lamourenx Christopher, William D. Lastrapes. Heteroskedasticity in Stock Return Data :Volume versus Garch Effects[J]. Journal of Finance,1990,45:221-229.
[37] Craig A. Depken. Good Bad News and GARCH Effects in Stock Return Data[J]. Journal of Applied Economics,1999,4:313-327.
[38] De Long, J.B., Shleifer A., Summers. L. G., Waldmann R. J.. Noise Trader Risk in Financial Markets[J]. Journal of Political Economy,1990,4:265-274.
[39] Brown, G.W.. Volatility, Sentiment and Noise Traders[J]. Financial Analysts Journal,1999, 55:1-27.
[40] Lee. W. J., Jiang. C. X., Indro. D. C.. Stock Market Volatility , Excess Returns and The Role of Investor Sentiment[J]. Journal of Banking &Finance,2002,26:2277-2299.
[41]童菲,冯涛.信息对波动性的影响:基于上海股市的实证检验[J].经济管理, 2005, 20: 74-80.
[42]王春峰,韩冬,蒋祥林.基于信息非对称模型的交易行为与波动性关系研究——交易规模和交易频率[J].管理工程学报,2007,01:134-136.
[43]凌士勤,杨波,袁开洪.分类信息对股市波动的影响研究[J].中国管理科学,2005,03:20-24.
[44]王美今,孙建军.中国股市收益、收益波动与投资者情绪[J].经济研究,2004,10:75-83.
[45]张强,杨淑娥,杨红.中国股市投资者情绪与股票收益的实证研究[J].系统工程,2007,07:13-16.
[46] Jeon B.N,Von Furstenberg G.M..Growing international co-movement in stock price indexes[J]. Quarterly Review of Economics and Business ,1990,3:121-127.
[47] Steven Shuye Wang, Michael Firth .Do bear and bulls swim across oceans?Market information transmission between greater China and the rest of the world[J] . Journal of International Financial Markets, Institutions and Money,2004,14(3):235-254.
[48] So R.W.,and Tse Y.,Price Discovery in the Hang-Sang Index and the Futures Market.Journal of Futures,and Tracker Fund[J].Journal of Futures Markets,2004,24:887-907.
[49]刘金全,崔畅.中国沪深股市收益率和波动性的实证分析[J].经济学(季刊), 2002,1(4):885-97.
[50]史代敏.沪深股票市场风险变异性实证研究[J].数量经济技术经济研究,2002,3:118-21.
[51]宿成建,刘星,刘礼培,魏锋.应用小波分析方法研究沪深股市的溢出效应[J].系统工程学报,2004,9(1):99-103.
[52]周孝华,黄赟.我国股票市场沪深两市波动性研究[J].经济与管理研究,2008,8:77-82.
[2] Engle R F.. Autoregressive Conditional Heteroskedasticity with Estimates of the VARiance of United Kingtom Inflation[J]. Econometrica,1982,50(7):203-224.
[3] R.F.Engel,D.M.Lilien,and R.P.Robins.Estimating time varing risk premia in the term structure: The ARCH-M model[J].Econometrica,1987,55:391-407.
[4] Bollerslev.T.Generalized regressive conditional heteroskedasticity[J].Journal of Econometrics, 1986,31(8):307-327.
[5] Engle,Robert F,David M.Lilien,and Russell P.Robins.Estimating time varying risk:premia in theterm structure.The ARCH-M model[J].Econometrics,1987,55(10):391-407.
[6] Glosten,L.R.,Jagan.nathan,R.Runkle,D.E.on the relation between the expected value and thevolatility of the norminal excess return on stocks[J]. Journal of Finance,1992, 48(12):1779-1801.
[7] Zakoian,J.M.Threshold Heteroskedastic model[J].Journal of Economic Dynamics and Control, 1994,18(4):931-955.
[8] Nelson,Daniel B.Conditional heteroskedasticity in Econometrics,asset returns:A new approach[J]. Econometrics,1990,59(11):347-370.
[9] Bernake,Mark.,Stock returns and volatility[J].Journal of Financial and Quantitative Analysis, 1991,25(6):203-215.
[10] Engle,Mustafa.Asset pricing with a factor ARCH covariance structure:empirical estimates for treasury bills[J].Journal of Econometrics,1992,45(10):213-238.
[11] French,Kenneth,G.William Schwert,Stambaugh.Expected stock returns and volatility[J]. Journal of Financial Economics,1987,19(5):3-30.
[12] Nelson,Daniel.B.Modeling stock market volatility change.Proceedings of the American[J]. Statistical Association Business and Economic Statistics Section,1989,7:93-98.
[13] G.William Schwert.Stock market volatility[J],Financial Analysis Journal,1990,5:23-34.
[14] Glosten L.R.,Jagannathan,D.Runkle,value and the volatility of the nominal finance,On the relationbetween the expected excess return on stocks[J]. Journal of stocks 1993, 48(11):1779-1801.
[15] Engle R.F.and Ng.V K.Measuring and testing the impact of news on volatility[J].Journal of Finance.1993,48(11);1749-1801.
[16] Crouhy and Rockinger.Volatility Clustering,Asymmetry and Hysteresis in Stock Returns: Inernational Evidence[J].Asia-Pacific Financial Markets,1997,4(11):1-35.
[17] Chiang and Doong.Emprical Analysis of Stock Returns and Volatility:Eviedence from Seven Asian Stock Markets Based on TAR-GARCH Model[J].Spinger Netherlands,2001, 17(11):301-318.
[18] Taylor S J.Modeling financial time series[J].Econoetria,1986,56(4):201-220.
[19] A Harvey,E Ruiz,N Shephard.Multivariate stochastic variance models[J].The Review of Economic,1994,61(2) :247-264.
[20] A Melino,SM Turnbull.Pricing foreign currency options with stochastic volatility[J].Journal of Econometrics,1990,45(2):239-265.
[21] D Duffle.Simulated moments estimation of Markov Modelsof asset prices[J]. Journal of Econometrics ,1993,61(4):929-952.
[22] KJ Singleton.Estimation of affine asset pricing models using the empirical characteristic function[J].Journal of Econometrics,2001,102(1):111-141.
[23] J.Danielsson and J.F.Richard.Accelerated Gaussian Importance Sampler With Application to Dynamic Latent Variable Models[J].Journal of Applied Econometrics,1993,8(12):153-173.
[24] J.Danielsson.Stochastic volatility in asset prices:Estimation with simulated maximum likelihood[J].Journal of econometrics,1994,64(2):375-400.
[25]陆金海.中国货币需求函数长期均衡实证分析[J].厦门大学学报, 2000,(1):32-36.
[26]吴长风.利用回归GARCH模型对我国沪深股市的分析[J].预测,1999,7(2):46-47.
[27]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,2000,16(5):24-27.
[28]孙培源,施东辉.跌幅限制降低了股价波动吗?——来自中国股票市场的证据[J].证券市场导报,2001,11:30-36.
[29]吴冲锋,刘海龙,吴文锋,穆启国.涨跌幅限制与股价变动研究.上证联合研究计划第三期课题,2001.
[30]许均华,李启亚.宏观政策对我国股市影响的实证研究[J].经济研究2001,9:38-45.
[31]陈浪南,黄杰鳗,中国股票市场波动非对称性的实证研究[J].金融研究,2002,(7):75-77.
[32]胡海鹏,方兆本.用AR-EGARCH-M模型对中国股市波动性的拟合分析[J].系统工程.2002, 18(7):32-36.
[33]李亚静,何跃,朱宏泉.中国股市收益率与波动长记忆性的实证研究[J].系统工程理论与实践2003(1):9-14.
[34]丁华.股价指数波动中的ARCH现象.数量经济与技术经济研究[J].2003,12(9):22-25.
[35] Peker K., Clark. A Subordinated Stochastic Process Model with Finite Variance ForSpeculative Prices[J]. Econometrica,1973,41:77-85.
[36] Lamourenx Christopher, William D. Lastrapes. Heteroskedasticity in Stock Return Data :Volume versus Garch Effects[J]. Journal of Finance,1990,45:221-229.
[37] Craig A. Depken. Good Bad News and GARCH Effects in Stock Return Data[J]. Journal of Applied Economics,1999,4:313-327.
[38] De Long, J.B., Shleifer A., Summers. L. G., Waldmann R. J.. Noise Trader Risk in Financial Markets[J]. Journal of Political Economy,1990,4:265-274.
[39] Brown, G.W.. Volatility, Sentiment and Noise Traders[J]. Financial Analysts Journal,1999, 55:1-27.
[40] Lee. W. J., Jiang. C. X., Indro. D. C.. Stock Market Volatility , Excess Returns and The Role of Investor Sentiment[J]. Journal of Banking &Finance,2002,26:2277-2299.
[41]童菲,冯涛.信息对波动性的影响:基于上海股市的实证检验[J].经济管理, 2005, 20: 74-80.
[42]王春峰,韩冬,蒋祥林.基于信息非对称模型的交易行为与波动性关系研究——交易规模和交易频率[J].管理工程学报,2007,01:134-136.
[43]凌士勤,杨波,袁开洪.分类信息对股市波动的影响研究[J].中国管理科学,2005,03:20-24.
[44]王美今,孙建军.中国股市收益、收益波动与投资者情绪[J].经济研究,2004,10:75-83.
[45]张强,杨淑娥,杨红.中国股市投资者情绪与股票收益的实证研究[J].系统工程,2007,07:13-16.
[46] Jeon B.N,Von Furstenberg G.M..Growing international co-movement in stock price indexes[J]. Quarterly Review of Economics and Business ,1990,3:121-127.
[47] Steven Shuye Wang, Michael Firth .Do bear and bulls swim across oceans?Market information transmission between greater China and the rest of the world[J] . Journal of International Financial Markets, Institutions and Money,2004,14(3):235-254.
[48] So R.W.,and Tse Y.,Price Discovery in the Hang-Sang Index and the Futures Market.Journal of Futures,and Tracker Fund[J].Journal of Futures Markets,2004,24:887-907.
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