中国股票市场量价关系的理论与实证研究
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摘要
本文以中国股票市场的价格波动与交易量为研究对象,重点对以下方面进行了研究:
1.在国内首次利用非对称成分GARCH-M模型对中国股票市场的量价关系进行了实证研究,并把交易量分解为预期交易量和非预期交易量。实证结果显示中国股票市场价格波动的短期成分主要由交易量解释,非预期交易量对市场波动的解释相比预期交易量具有绝对优势,说明交易量中的非预期成分所替代的市场信息是真正引起价格波动的根源,这与国际上广泛流行的“混合分布假说”理论是一致的。另外,中国股票市场短期波动的持续性只能由加入的交易量部分解释,除了交易量外,还有其它的因素会引起股票价格的短期波动,这与新兴股票市场的研究结论相似,而与美国等成熟资本市场的结论不一致,美国学者研究成果显示其股票市场的价格波动可由交易量完全解释。在量价关系的建模过程中考虑了滞后收益冲击对未来波动的影响,结论显示1997年之后的中国股票市场,负收益(利空消息)比相同程度的正收益(利好消息)对市场波动的冲击更大,即反应了中国股票市场存在杠杆效应。我们还研究了非预期交易量对市场波动的非对称影响,得出正的非预期交易量(放量)比同等程度的负的非预期交易量(缩量)对市场波动的影响更大,从而能引发更大的市场波动。这在一定程度上支持了投资者根据量价指标进行技术分析的投资策略,但和美国股票市场的相关研究结论相比,中国股票市场的非对称性差异更加显著,这反应了中国的投资者更加倾向于投机行为,大部分投资者都喜欢在市场交投活跃时进行投机交易,希望在短期内获得高的市场回报,也说明我国的投资者在投资理念和成熟的资本市场国家的投资者相比仍存在较大差距。
2.首次引入了研究量价关系的动态二元混合分布模型,并使用基于MCMC模拟技术的贝叶斯方法对模型参数进行估计。模型中的交易量作为量价系统的内生变量出现,从而弥补了传统建模的不足。实证研究结果显示:动态二元混合分布模型很大程度上能够捕捉收益波动的持续性特征;交易量由信息交易和噪声交易构成,而交易量的系统变动主要是由于信息交易部分的变动产生的。二元混合模型存在局限性,其原因可能是模型的假定条件过于苛刻。而后引入了广义二元混合分布模型,并进行了扩展,添加了反应投资者对市场新信息的敏感性具有时变性这一重要的潜在因素,事实证明广义二元混合模型显著拒绝了投资者对新信息的敏感度是常量的假定,市场信息与投资者对信息的敏感性都是决定量价动态关系的重要潜在因素。广义二元混合模型明显优于标准二元混合模型。
3.首次引入了一种广义混合分布假说理论,并检验其是否能够解释中国股票市场收益的ARCH效应和交易量的关系。结果显示,日收益波动包含很大的随机成分,能够解释超过总体一半的波动。非预期波动成分是由于信息流对市场的冲击产生的,而预期波动主要由滞后的收益冲击所驱动。
4.利用传统的Granger因果检验模型检验了中国股票市场交易量对收益的信息含量,结果表明,收益和交易量存在双向的反馈关系,过去交易量能够提供未来价格波动的信息,包括价格变动的幅度以及价格变动的方向,这与股市交易中的技术分析策略是一致的。
This dissertation takes a close and deep look at price-volume relation of China’s Stock Market and its main subjects are as follows:
1.Using Asymmetric Component GARCH model (AC-GARCH) to investigate price-volume relationship in china’s stock market and divide volume into expected and unexpected component. The empirical results show the transitory volatility is explained mainly due to unexpected volume, the daily information flow is the source of market volatility replaced by unexpected volume, and this proved the prevailing MDH theory. The volatility persistence is only partially explained by volume, other factors are also too. This is consistent with the emerging markets, but not with the USA. Whose market volatility is explained by volume completely. Volatility in equity markets is asymmetric: the results indicate that the impact of shock from negative returns and increased trading volume on volatility is larger than that from positive returns and decreased trading volume in the same magnitude, respectively after 1997 in China’s Stock Markets, especially in Shen Zhen city.
2. having introduced dynamic Bivariate Mixture Distribution models (BMD), including standard and modified mixture models, the volume in these models appears endogenous. Parameters of these models are then estimated with MCMC method based on Gibbs sampling. The results show that the BMD models can capture the persistence of return volatility basically, and prove the daily trading volume has informed and noise components. The systematic variation in trading volume is due solely to fluctuations in the informed volume. But the defects in these models exist too, may attributing mainly to strict limitation hypothesis conditions of the models. Next a Generalized bivariate mixture model for stock price volatility and trading volume is used to analyze stock price volatility and trading volume on the China Stock Market. In this model, the traders’sensitivity to new information is traded as an important latent factor. The empirical results based on daily data of individual stocks reveals that the standard mixture model with its assumption that traders’sensitivity to new information is constant over time is clearly rejected against the generalized model. The number of information arrivals as well as the sensetivity to news are important factors accounting for the relationships between price volatility and trading volume. The genaralized mixture model improves obviously the explaination of the behavior of volatility relative to the standard model.
3. proposing a generalized mixture of distribution hypothesis (GMDH) and examine whether it explains the relation between ARCH effects and trading volume. The empirical conclusion shows that daily volatility contains a stochastic component that typically accounts for over half of its overall variation. The unexpected component of daily volatility stems from daily information releases and the expected component is driven by past return shocks.
4. Linear Granger causality tests conducted to detect information content of volume. The results show that the bidireactional causality is supported between volume and returns. Statistical analysis shows that volume conveys information to the market about the magnitude of price changes and the direction of price changes. This is in accordance with technical analysis of trading strategy.
1.在国内首次利用非对称成分GARCH-M模型对中国股票市场的量价关系进行了实证研究,并把交易量分解为预期交易量和非预期交易量。实证结果显示中国股票市场价格波动的短期成分主要由交易量解释,非预期交易量对市场波动的解释相比预期交易量具有绝对优势,说明交易量中的非预期成分所替代的市场信息是真正引起价格波动的根源,这与国际上广泛流行的“混合分布假说”理论是一致的。另外,中国股票市场短期波动的持续性只能由加入的交易量部分解释,除了交易量外,还有其它的因素会引起股票价格的短期波动,这与新兴股票市场的研究结论相似,而与美国等成熟资本市场的结论不一致,美国学者研究成果显示其股票市场的价格波动可由交易量完全解释。在量价关系的建模过程中考虑了滞后收益冲击对未来波动的影响,结论显示1997年之后的中国股票市场,负收益(利空消息)比相同程度的正收益(利好消息)对市场波动的冲击更大,即反应了中国股票市场存在杠杆效应。我们还研究了非预期交易量对市场波动的非对称影响,得出正的非预期交易量(放量)比同等程度的负的非预期交易量(缩量)对市场波动的影响更大,从而能引发更大的市场波动。这在一定程度上支持了投资者根据量价指标进行技术分析的投资策略,但和美国股票市场的相关研究结论相比,中国股票市场的非对称性差异更加显著,这反应了中国的投资者更加倾向于投机行为,大部分投资者都喜欢在市场交投活跃时进行投机交易,希望在短期内获得高的市场回报,也说明我国的投资者在投资理念和成熟的资本市场国家的投资者相比仍存在较大差距。
2.首次引入了研究量价关系的动态二元混合分布模型,并使用基于MCMC模拟技术的贝叶斯方法对模型参数进行估计。模型中的交易量作为量价系统的内生变量出现,从而弥补了传统建模的不足。实证研究结果显示:动态二元混合分布模型很大程度上能够捕捉收益波动的持续性特征;交易量由信息交易和噪声交易构成,而交易量的系统变动主要是由于信息交易部分的变动产生的。二元混合模型存在局限性,其原因可能是模型的假定条件过于苛刻。而后引入了广义二元混合分布模型,并进行了扩展,添加了反应投资者对市场新信息的敏感性具有时变性这一重要的潜在因素,事实证明广义二元混合模型显著拒绝了投资者对新信息的敏感度是常量的假定,市场信息与投资者对信息的敏感性都是决定量价动态关系的重要潜在因素。广义二元混合模型明显优于标准二元混合模型。
3.首次引入了一种广义混合分布假说理论,并检验其是否能够解释中国股票市场收益的ARCH效应和交易量的关系。结果显示,日收益波动包含很大的随机成分,能够解释超过总体一半的波动。非预期波动成分是由于信息流对市场的冲击产生的,而预期波动主要由滞后的收益冲击所驱动。
4.利用传统的Granger因果检验模型检验了中国股票市场交易量对收益的信息含量,结果表明,收益和交易量存在双向的反馈关系,过去交易量能够提供未来价格波动的信息,包括价格变动的幅度以及价格变动的方向,这与股市交易中的技术分析策略是一致的。
This dissertation takes a close and deep look at price-volume relation of China’s Stock Market and its main subjects are as follows:
1.Using Asymmetric Component GARCH model (AC-GARCH) to investigate price-volume relationship in china’s stock market and divide volume into expected and unexpected component. The empirical results show the transitory volatility is explained mainly due to unexpected volume, the daily information flow is the source of market volatility replaced by unexpected volume, and this proved the prevailing MDH theory. The volatility persistence is only partially explained by volume, other factors are also too. This is consistent with the emerging markets, but not with the USA. Whose market volatility is explained by volume completely. Volatility in equity markets is asymmetric: the results indicate that the impact of shock from negative returns and increased trading volume on volatility is larger than that from positive returns and decreased trading volume in the same magnitude, respectively after 1997 in China’s Stock Markets, especially in Shen Zhen city.
2. having introduced dynamic Bivariate Mixture Distribution models (BMD), including standard and modified mixture models, the volume in these models appears endogenous. Parameters of these models are then estimated with MCMC method based on Gibbs sampling. The results show that the BMD models can capture the persistence of return volatility basically, and prove the daily trading volume has informed and noise components. The systematic variation in trading volume is due solely to fluctuations in the informed volume. But the defects in these models exist too, may attributing mainly to strict limitation hypothesis conditions of the models. Next a Generalized bivariate mixture model for stock price volatility and trading volume is used to analyze stock price volatility and trading volume on the China Stock Market. In this model, the traders’sensitivity to new information is traded as an important latent factor. The empirical results based on daily data of individual stocks reveals that the standard mixture model with its assumption that traders’sensitivity to new information is constant over time is clearly rejected against the generalized model. The number of information arrivals as well as the sensetivity to news are important factors accounting for the relationships between price volatility and trading volume. The genaralized mixture model improves obviously the explaination of the behavior of volatility relative to the standard model.
3. proposing a generalized mixture of distribution hypothesis (GMDH) and examine whether it explains the relation between ARCH effects and trading volume. The empirical conclusion shows that daily volatility contains a stochastic component that typically accounts for over half of its overall variation. The unexpected component of daily volatility stems from daily information releases and the expected component is driven by past return shocks.
4. Linear Granger causality tests conducted to detect information content of volume. The results show that the bidireactional causality is supported between volume and returns. Statistical analysis shows that volume conveys information to the market about the magnitude of price changes and the direction of price changes. This is in accordance with technical analysis of trading strategy.
引文
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