中国股票与债券市场价格联动研究
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摘要
本文研究了我国2000-2011年间股票与债券市场价格联动关系,并分析了证券投资基金对股票与债券市场价格联动的影响能力。论文首先对股票和债券市场的发展进行了系统的分析,并分别对股票和债券市场价格的波动性进行了描述性统计和ARCH模型分析；然后,用VAR和二元GARCH模型对股票债券市场价格联动进行分析论证；最后,对我国证券投资基金的发展进行了描述,分析了证券投资基金的指数收益率、投资组合和羊群行为的特征并用实证方法论证了这些行为对股债价格联动的影响作用。论文的主要结论如下：
第一,股票与债券市场在证券发行、筹资及交易量等都实现了相对平稳的增长,并取得了显著的发展成果。而同期股票与债券市场价格却具有较强的波动性,并且这种波动性具有时变性和非对称性。结论也表明,股票价格的波动性大于债券价格的波动性,深市价格的波动性大于沪市价格的波动性,企债价格的波动性又大于国债价格的波动性。
第二,全样本VAR模型表明股债价格之间存在程度较低的相互影响关系,且这种关系具有非对称性,即债券价格对股票价格的影响作用大于股票价格对债券价格的影响作用；子样本VAR模型表明股债价格联动具有时变性；二元GARCH模型也证明了股债价格之间存在明显的联动关系,且这种关系是非对称的和时变的。
第三,对证券投资基金的指数收益率分析表明证券投资基金与股票价格存在较强的相互关系,而与债券市场价格存在较弱的相互关系；证券投资基金的指数收益率、投资组合的构成及其所具有的羊群行为单独或同时作为解释变量都对作为被解释变量的股债价格联动具有比较显著的解释能力,这说明证券投资基金具有影响股票与债券市场价格的能力。
This paper studies price comovement of China's stock and bond markets from2000to2011, and analyzes that behavior of securities investment fund (Abbreviates"SIF") had impact on the comovement. The paper describes the development process of the stock and bond markets, and analyzes price volatility of the stock and bond markets through descriptive analysis and ARCH model; then, studies the price comovement of stock and bond markets by VAR and bivariate GARCH model; finally, describes the development of SIF, and analyzes if SIF's bahaviour including rate of return, portfolio and herding behavior had impact on price comovement of stock and bond markets. The main conclusion is as follows:
First, the issuance, financing and trade of the stock and bond markets had achieved relatively smooth growth and remarkable results.But the stock and bond market prices were high volatility, and this volatility was time-varying and Asymmetry. The conclusion also shows that the volatility of the stock market price was greater than the bond market price, volatility of Shenzhen Stock Exchange was greater than volatility of the Shanghai Stock Exchange, volatility of Treasury bill was greater than volatility of corporate bonds.
Secondly, the full sample VAR model shows that a low degree of relationship between the stock and bond prices, and this relationship was asymmetric, i.e., the impact of bond price on stock price was greater than the impact of stock price on bond price;the sub-sample VAR model indicates price comovement of stock and bond was time-varying; bivariate GARCH model also proves that price comovement of stock and bond market existed, and the comovement was asymmetric and time-varying.
Thirdly, analysis of SIF's index shows that there was a strong relationship between SIF's index and stock market price, and there was a weak relationship between SIF's index and bond market price; rate of return, portfolio and herding behavior of SIF had effect on price comovement of stock and bond market, which indicate that SIF had an impact on stock and bond market price.
第一,股票与债券市场在证券发行、筹资及交易量等都实现了相对平稳的增长,并取得了显著的发展成果。而同期股票与债券市场价格却具有较强的波动性,并且这种波动性具有时变性和非对称性。结论也表明,股票价格的波动性大于债券价格的波动性,深市价格的波动性大于沪市价格的波动性,企债价格的波动性又大于国债价格的波动性。
第二,全样本VAR模型表明股债价格之间存在程度较低的相互影响关系,且这种关系具有非对称性,即债券价格对股票价格的影响作用大于股票价格对债券价格的影响作用；子样本VAR模型表明股债价格联动具有时变性；二元GARCH模型也证明了股债价格之间存在明显的联动关系,且这种关系是非对称的和时变的。
第三,对证券投资基金的指数收益率分析表明证券投资基金与股票价格存在较强的相互关系,而与债券市场价格存在较弱的相互关系；证券投资基金的指数收益率、投资组合的构成及其所具有的羊群行为单独或同时作为解释变量都对作为被解释变量的股债价格联动具有比较显著的解释能力,这说明证券投资基金具有影响股票与债券市场价格的能力。
This paper studies price comovement of China's stock and bond markets from2000to2011, and analyzes that behavior of securities investment fund (Abbreviates"SIF") had impact on the comovement. The paper describes the development process of the stock and bond markets, and analyzes price volatility of the stock and bond markets through descriptive analysis and ARCH model; then, studies the price comovement of stock and bond markets by VAR and bivariate GARCH model; finally, describes the development of SIF, and analyzes if SIF's bahaviour including rate of return, portfolio and herding behavior had impact on price comovement of stock and bond markets. The main conclusion is as follows:
First, the issuance, financing and trade of the stock and bond markets had achieved relatively smooth growth and remarkable results.But the stock and bond market prices were high volatility, and this volatility was time-varying and Asymmetry. The conclusion also shows that the volatility of the stock market price was greater than the bond market price, volatility of Shenzhen Stock Exchange was greater than volatility of the Shanghai Stock Exchange, volatility of Treasury bill was greater than volatility of corporate bonds.
Secondly, the full sample VAR model shows that a low degree of relationship between the stock and bond prices, and this relationship was asymmetric, i.e., the impact of bond price on stock price was greater than the impact of stock price on bond price;the sub-sample VAR model indicates price comovement of stock and bond was time-varying; bivariate GARCH model also proves that price comovement of stock and bond market existed, and the comovement was asymmetric and time-varying.
Thirdly, analysis of SIF's index shows that there was a strong relationship between SIF's index and stock market price, and there was a weak relationship between SIF's index and bond market price; rate of return, portfolio and herding behavior of SIF had effect on price comovement of stock and bond market, which indicate that SIF had an impact on stock and bond market price.
引文
1“联动”对应的英文是‘"Comovement",其理论并不十分复杂和高深,近些年经常出现在研究不同金融资产和不同市场之间相关性(包括价格、收益、流动性)文献的标题中。如：
①enneth F Kroner, Victor K Ng. Modeling Asymmetric Comovements of Asset Return. The Review of Financial Studies,1998,11:817-844;
②Robert A. Connolly, Chris Stivers and Licheng Sun. Commonality in The Time variation of Stock-stock and Stock-bond Comovements. Journal of Financial Markets,2007,10:192-218;
③iao-Ming Li, Li-Ping Zou. How Do Policy and Information Shocks Impact Co-movements of China's T-bond and Stock Markets? Journal of Banking & Finance,2008,32:347-359;
④Lars Norden, Martin Weber. The Comovement of Credit Default Swap, Bond and Stock Markets: an Empirical Analysis. European Financial Management,2009,15:529-562;等等。
2见Amil Dasgupta, Andrea Prat, Michela Verardo. The Price Impact of Institutional Herding. The Review of Financial Studies,2011,24:892-925.
6见Amil Dasgupta, Andrea Prat, Michela Verardo. Institutional Trade Persistence and Long-Term Equity Returns. Journal of Finance,2011,66:635-663.
8股票市场发展阶段的划分借鉴了：中国证监会.中国资本市场发展报告.中国金融出版社,2008,1-34.
9债券市场发展阶段的划分借鉴了：马庆泉等.中国证券史·第2卷(1999-2007年).中国金融出版社,2008,206-266；中国证监会.中国资本市场发展报告.中国金融出版社,2008,1-34.
11 ARCH、GARCH和TARCH模型的公式借鉴了：高铁梅.计量经济分析方法与建模EViews应用及实例.清华大学出版社,第二版,2009,193-216.
12本节中VAR模型公式的介绍借鉴了：高铁梅.计量经济分析方法与建模EViews应用及实例.清华大学出版社,第二版,2009,267-270.
14脉冲反应理论借鉴了：高铁梅.计量经济分析方法与建模EViews应用及实例.清华大学出版社,第二版,2009,281-285.
15方差分解理论借鉴了：高铁梅.计量经济分析方法与建模EViews应用及实例.清华大学出版社,第二版,2009,288-290.
16多元GARCH模型主要借鉴了：Kenneth F.Kroner and Victor K.Ng. Modeling Asymmetric Comovements of Asset Returns. The Review of Financial Studies,1998,11:817-844;易丹辉.数据分析与EVIEWS应用.中国人民大学出版社,2008,283-284；蔡瑞胸著,王辉译.金融时间序列分析.人民邮电出版社,2009,389-393.
18此定义见：中国证券业协会.证券投资基金.中国财政经济出版社,2011,1.
19此部分内容借鉴了：中国证券业协会.证券投资基金.中国财政经济出版社,2011,14-21.
21详见Wermers R. Herding, Trade Reversals and Cascading by Institutional Investors. Mimeo, University of Colorado,1995.
22CH和CSSD是同一模型的两种不同称谓,CH是以两作者名字的首字母命名,CSSD是以研究对象命名。详见Christie W R, D Huang. Following the Pied Piper:Do Individual Returns Herd around The Market? Financial Analysts Journal,1995,8:31-37.
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①enneth F Kroner, Victor K Ng. Modeling Asymmetric Comovements of Asset Return. The Review of Financial Studies,1998,11:817-844;
②Robert A. Connolly, Chris Stivers and Licheng Sun. Commonality in The Time variation of Stock-stock and Stock-bond Comovements. Journal of Financial Markets,2007,10:192-218;
③iao-Ming Li, Li-Ping Zou. How Do Policy and Information Shocks Impact Co-movements of China's T-bond and Stock Markets? Journal of Banking & Finance,2008,32:347-359;
④Lars Norden, Martin Weber. The Comovement of Credit Default Swap, Bond and Stock Markets: an Empirical Analysis. European Financial Management,2009,15:529-562;等等。
2见Amil Dasgupta, Andrea Prat, Michela Verardo. The Price Impact of Institutional Herding. The Review of Financial Studies,2011,24:892-925.
6见Amil Dasgupta, Andrea Prat, Michela Verardo. Institutional Trade Persistence and Long-Term Equity Returns. Journal of Finance,2011,66:635-663.
8股票市场发展阶段的划分借鉴了：中国证监会.中国资本市场发展报告.中国金融出版社,2008,1-34.
9债券市场发展阶段的划分借鉴了：马庆泉等.中国证券史·第2卷(1999-2007年).中国金融出版社,2008,206-266；中国证监会.中国资本市场发展报告.中国金融出版社,2008,1-34.
11 ARCH、GARCH和TARCH模型的公式借鉴了：高铁梅.计量经济分析方法与建模EViews应用及实例.清华大学出版社,第二版,2009,193-216.
12本节中VAR模型公式的介绍借鉴了：高铁梅.计量经济分析方法与建模EViews应用及实例.清华大学出版社,第二版,2009,267-270.
14脉冲反应理论借鉴了：高铁梅.计量经济分析方法与建模EViews应用及实例.清华大学出版社,第二版,2009,281-285.
15方差分解理论借鉴了：高铁梅.计量经济分析方法与建模EViews应用及实例.清华大学出版社,第二版,2009,288-290.
16多元GARCH模型主要借鉴了：Kenneth F.Kroner and Victor K.Ng. Modeling Asymmetric Comovements of Asset Returns. The Review of Financial Studies,1998,11:817-844;易丹辉.数据分析与EVIEWS应用.中国人民大学出版社,2008,283-284；蔡瑞胸著,王辉译.金融时间序列分析.人民邮电出版社,2009,389-393.
18此定义见：中国证券业协会.证券投资基金.中国财政经济出版社,2011,1.
19此部分内容借鉴了：中国证券业协会.证券投资基金.中国财政经济出版社,2011,14-21.
21详见Wermers R. Herding, Trade Reversals and Cascading by Institutional Investors. Mimeo, University of Colorado,1995.
22CH和CSSD是同一模型的两种不同称谓,CH是以两作者名字的首字母命名,CSSD是以研究对象命名。详见Christie W R, D Huang. Following the Pied Piper:Do Individual Returns Herd around The Market? Financial Analysts Journal,1995,8:31-37.
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