我国股票市场波动非对称特性的研究
摘要
波动是股票市场最为重要的特征之一。目前国外学者对股市波动性研究的重点正逐渐从波动的持续性和簇丛性转移到波动的非对称性。我国学者在研究中也发现我国股票市场也存在波动非对称现象。
国外学者都将股市波动非对称定义为坏消息造成的冲击大于好消息的冲击,提出了杠杆假说和波动反馈说来解释波动非对称现象的成因,并创建了非对称Garch模型和非对称SV模型来检测波动非对称现象是否存在。国内的有些学者也开始关注到这个问题,但绝大数学者仅仅是对我国股市是否存在波动非对称现象进行了实证分析,而很少有人对我国股市波动非对称现象的特点和成因进行研究。本文作者认为,目前对波动非对称现象的定义不够准确和全面,已有的两个假说不能完全解释波动非对称现象的成因,而且波动非对称现象对监管者和投资者的借鉴意义也较少被探讨。
本文以我国股市的波动非对称现象为研究对象,探寻我国股市波动非对称性的特点,并在此基础上对波动非对称性进行更为准确和全面的界定和解释。
首先,本文通过实证分析发现,我国股市不仅存在负面消息的冲击大于正面消息,而且还存在正面消息的冲击大于负面消息,不仅股市中存在波动非对称现象,而且在债券市场和期货市场也存在波动非对称现象。因此,本文认为股市波动非对称性不等于杠杆效应,也不能象国外学者那样将其仅仅定义为负面消息的冲击大于正面消息。所以本文提出波动非对称性应该定义为正面消息产生的冲击和负面消息产生的冲击存在显著差异。基于新的定义本文对波动非对称现象的金融经济学含义进行了探讨,认为信息冲击曲线不仅仅有V字型还应该有S型,并制定了一些指标来度量波动不对称程度。
其次,本文对杠杆假说和波动反馈说的理论逻辑进行了分析,发现这两种假说都不能解释好消息的冲击大于坏消息的冲击,对其是否适用我国进行了实证检验。(1)分别运用我国股票市场横截面数据和时间序列数据对多维度的杠杆比例指标和股市波动非对称性之间的关系进行了实证分析,发现杠杆比率对股票市场波动非对称性之间没有显著影响,杠杆假说成立的前提也不完全满足,因此可以认为杠杆说难以解释我国股票市场的波动非对称性;(2)本文通过研究发现波动反馈说在理论上不能解释个股信息冲击也存在波动非对称性。通过对波动反馈说成立必要条件的直接检验和间接检验后发现这些条件难以满足,因此可以该假说也不能解释我国股市波动非对称性的成因。
再次,本文创新性地对上市公司风格特征、市道和投资者性质差异对我国股市波动非对称是否有显著影响进行了深入研究,发现这些因素对波动非对称性存在显著影响。(1)实证研究发现市道对波动非对称方向具有显著的影响,即牛市中存在正向波动不对称,熊市中存在负向波动不对称。通过运用行为金融学理论分析发现,市道通过影响投资者情绪,进而影响投资者对信息的理解和投资行为,最终导致波动非对称产生,因此本文提出了市道说来解释我国股市波动非对称的成因。通过利息调整、股改和扩容对市场冲击的这三个具体案例也充分说明市道说可以解释我国股市波动非对称性的成因。(2)实证分析发现在牛市中成长股的波动不对称程度要显著大于价值股的不对称程度,然而在熊市中成长股的波动不对称程度和价值股没有显著差异。另外本文发现所有的多维度的公司规模度量指标和波动非对称性都不存在显著相关性。(3)实证研究发现同一家上市公司A股和B股的波动不对称性存在显著差异。另外,实证研究还发现在牛市中机构投资者重仓股票的波动非对称性和个人投资者重仓股票的波动非对称性无显著差异,而在熊市中说明机构持仓越多的股票,其波动非对称性越低,个人投资者集中投资的股票的波动非对称性较高。
最后本文通过分析发现考虑波动不对称性能提高对股市收益率特征值和未来波动率估计的准确性,而且还能提高动态VaR估计的有效性。对波动非对称性的研究也能够促进政策监管层把握政策出台时机和信息发布时机的能力以促进市场的稳定、提高上市公司提高融资效率和提高投资者的投资管理效率和风险管理效率。
本文在以下方面有所突破:(1)对股票市场波动非对称性的界定更为清楚、准确和全面;(2)率先从多度角度对我国股市波动非对称性的成因进行全面研究,不仅对杠杆假说和波动反馈说这两种假说进行检验,而且还创新性地考察了上市公司风格特征、投资者差异和市道等因素对我国股市波动非对称性的影响;(3)率先从行为金融学的角度构建了市道说模型来解释中国股票市场的波动非对称现象;(4)探讨了波动非对称现象对监管者、投资者和上市公司的借鉴意义。
Stock price always fluctuates, and the volatility is one of the most important concepts as well. Recently, foreign researchers transfer their attention from volatility persistence and volatility cluster to volatility asymmetry. Domestic researchers have found asymmetric volatility exists in China's stock market, which the impacts show asymmetric property from good news and bad one.
Foreign researches find the volatility of developed countries' and development countries' stock market is asymmetric. All researchers define the volatility asymmetry as the impact of bad news is larger than good news and suggest leverage hypothesis and volatility feedback story to explain the asymmetry, and create asymmetric Garch model and asymmetric SV model to detect volatility asymmetry effect. Domestic researchers begin to study the phenomena, but most people just have empirically studied whether our own stock market had volatility asymmetry effect and almost no one investigates the character and reason of this effect. I believe this definition is not correct, and these two hypotheses couldn't well explain all the reasons of volatility's asymmetry, and few people puts interest into the study what kind of suggestion could get from the phenomena.
This article focuses on volatility asymmetry of China's stock market, investigates the character of the asymmetry, and finally makes a more correct definition and deeper research of it and its origin.
First of all, after empirical examination I find the impact of bad news is larger than good news in China's stock market at most of time while these exists specially opposite case. Besides stock market, volatility asymmetry also appears in bond market and future market. Therefore, volatility asymmetry is not a leverage effect, and it can't be simply defined as the impact that bad news is larger than good news. And so I think volatility asymmetry should be defined as the significant difference between the impact of good news and bad one. In this article I also probe into the financial economic meanings of volatility asymmetry and find the information impact curve could be either in V shape or in S shape. And I cite some indices to measure the asymmetric degree of volatility as well.
Secondly, I examine leverage hypothesis and volatility feedback story whether explain the volatility asymmetry phenomena in the following two ways: (1) I find leverage hypothesis cann't explain why the impact of good news sometimes is larger than bad news and make empirical analysis on the section data and time series data of our stock market, and find the correlation between leverage ratios and volatility asymmetry is not significant and the precondition of leverage hypothesis is not fulfilled. And I conclude that this hypothesis can not explain the asymmetry. (2) After analyzing the logic of the volatility feedback story, I find it could not explain the asymmetry at firm level and the reason why sometimes the impact of good news is larger than bad news. I compare the asymmetric degree of high beta stocks with low beta stocks to find only in bearish market the correlation is significant and positive. And it indicates the necessary conditions of this theory can not be satisfied. Totally, this story can not explain the asymmetry of China's stock market.
Thirdly, I start my study on whether stock style, market status and character of investor influence the asymmetry and find the influence is relatively significant in the following three aspects: (1) Empirical studies show the asymmetry is positive in bullish market and negative in bearish one. By behavioral finance analysis, we find market status influences investor's mood, causes understanding of information and reacting to its irrationality and results in volatility asymmetry. And so I recommend the market status hypothesis to explain the asymmetry and support three cases to prove, such as interest rate adjustment, stock circulating institution reform, and IPO and refinance,. (2) We find the asymmetric degree of volatility of growth stocks is larger than value stocks in bullish market, but in bearish they have no significant difference. Also I have analyzed the correlation between market value and the asymmetric degree, but results show they are not significant. (3) Empirical studies indicate there is significant difference between A shares and B shares of the same company. The asymmetric degree of overweight stock of institutional investors does not differentiate with personal investors' in bullish, while the volatility and asymmetry of overweight stock of institutional investors is less than personal investors in bearish.
Finally, if considering volatility asymmetry we can estimate the statistical characteristics of stock return, forecast the volatility of stock and calculate the dynamic value at risk (VaR) accurately. Studies on the asymmetry can promote the regulator to carry out new polices and disclose new information in appropriate time, such it could enhance the stability of the market and financial efficiency of company, and improve investors to manage portfolio and risk more efficiently.
Four contributions are made in this article: (1) make a more accurate definition of volatility asymmetry. (2) firstly investigate the reasons of the asymmetry of China's stock market in several angles including leverage ratios, volatility feedback, stock style, market status and character of investors. (3) construct a new model with market status hypothesis to explain the asymmetry in behavioral finance angle. (4) discuss the meaning of volatility asymmetry to regulators, investors and listed company.
国外学者都将股市波动非对称定义为坏消息造成的冲击大于好消息的冲击,提出了杠杆假说和波动反馈说来解释波动非对称现象的成因,并创建了非对称Garch模型和非对称SV模型来检测波动非对称现象是否存在。国内的有些学者也开始关注到这个问题,但绝大数学者仅仅是对我国股市是否存在波动非对称现象进行了实证分析,而很少有人对我国股市波动非对称现象的特点和成因进行研究。本文作者认为,目前对波动非对称现象的定义不够准确和全面,已有的两个假说不能完全解释波动非对称现象的成因,而且波动非对称现象对监管者和投资者的借鉴意义也较少被探讨。
本文以我国股市的波动非对称现象为研究对象,探寻我国股市波动非对称性的特点,并在此基础上对波动非对称性进行更为准确和全面的界定和解释。
首先,本文通过实证分析发现,我国股市不仅存在负面消息的冲击大于正面消息,而且还存在正面消息的冲击大于负面消息,不仅股市中存在波动非对称现象,而且在债券市场和期货市场也存在波动非对称现象。因此,本文认为股市波动非对称性不等于杠杆效应,也不能象国外学者那样将其仅仅定义为负面消息的冲击大于正面消息。所以本文提出波动非对称性应该定义为正面消息产生的冲击和负面消息产生的冲击存在显著差异。基于新的定义本文对波动非对称现象的金融经济学含义进行了探讨,认为信息冲击曲线不仅仅有V字型还应该有S型,并制定了一些指标来度量波动不对称程度。
其次,本文对杠杆假说和波动反馈说的理论逻辑进行了分析,发现这两种假说都不能解释好消息的冲击大于坏消息的冲击,对其是否适用我国进行了实证检验。(1)分别运用我国股票市场横截面数据和时间序列数据对多维度的杠杆比例指标和股市波动非对称性之间的关系进行了实证分析,发现杠杆比率对股票市场波动非对称性之间没有显著影响,杠杆假说成立的前提也不完全满足,因此可以认为杠杆说难以解释我国股票市场的波动非对称性;(2)本文通过研究发现波动反馈说在理论上不能解释个股信息冲击也存在波动非对称性。通过对波动反馈说成立必要条件的直接检验和间接检验后发现这些条件难以满足,因此可以该假说也不能解释我国股市波动非对称性的成因。
再次,本文创新性地对上市公司风格特征、市道和投资者性质差异对我国股市波动非对称是否有显著影响进行了深入研究,发现这些因素对波动非对称性存在显著影响。(1)实证研究发现市道对波动非对称方向具有显著的影响,即牛市中存在正向波动不对称,熊市中存在负向波动不对称。通过运用行为金融学理论分析发现,市道通过影响投资者情绪,进而影响投资者对信息的理解和投资行为,最终导致波动非对称产生,因此本文提出了市道说来解释我国股市波动非对称的成因。通过利息调整、股改和扩容对市场冲击的这三个具体案例也充分说明市道说可以解释我国股市波动非对称性的成因。(2)实证分析发现在牛市中成长股的波动不对称程度要显著大于价值股的不对称程度,然而在熊市中成长股的波动不对称程度和价值股没有显著差异。另外本文发现所有的多维度的公司规模度量指标和波动非对称性都不存在显著相关性。(3)实证研究发现同一家上市公司A股和B股的波动不对称性存在显著差异。另外,实证研究还发现在牛市中机构投资者重仓股票的波动非对称性和个人投资者重仓股票的波动非对称性无显著差异,而在熊市中说明机构持仓越多的股票,其波动非对称性越低,个人投资者集中投资的股票的波动非对称性较高。
最后本文通过分析发现考虑波动不对称性能提高对股市收益率特征值和未来波动率估计的准确性,而且还能提高动态VaR估计的有效性。对波动非对称性的研究也能够促进政策监管层把握政策出台时机和信息发布时机的能力以促进市场的稳定、提高上市公司提高融资效率和提高投资者的投资管理效率和风险管理效率。
本文在以下方面有所突破:(1)对股票市场波动非对称性的界定更为清楚、准确和全面;(2)率先从多度角度对我国股市波动非对称性的成因进行全面研究,不仅对杠杆假说和波动反馈说这两种假说进行检验,而且还创新性地考察了上市公司风格特征、投资者差异和市道等因素对我国股市波动非对称性的影响;(3)率先从行为金融学的角度构建了市道说模型来解释中国股票市场的波动非对称现象;(4)探讨了波动非对称现象对监管者、投资者和上市公司的借鉴意义。
Stock price always fluctuates, and the volatility is one of the most important concepts as well. Recently, foreign researchers transfer their attention from volatility persistence and volatility cluster to volatility asymmetry. Domestic researchers have found asymmetric volatility exists in China's stock market, which the impacts show asymmetric property from good news and bad one.
Foreign researches find the volatility of developed countries' and development countries' stock market is asymmetric. All researchers define the volatility asymmetry as the impact of bad news is larger than good news and suggest leverage hypothesis and volatility feedback story to explain the asymmetry, and create asymmetric Garch model and asymmetric SV model to detect volatility asymmetry effect. Domestic researchers begin to study the phenomena, but most people just have empirically studied whether our own stock market had volatility asymmetry effect and almost no one investigates the character and reason of this effect. I believe this definition is not correct, and these two hypotheses couldn't well explain all the reasons of volatility's asymmetry, and few people puts interest into the study what kind of suggestion could get from the phenomena.
This article focuses on volatility asymmetry of China's stock market, investigates the character of the asymmetry, and finally makes a more correct definition and deeper research of it and its origin.
First of all, after empirical examination I find the impact of bad news is larger than good news in China's stock market at most of time while these exists specially opposite case. Besides stock market, volatility asymmetry also appears in bond market and future market. Therefore, volatility asymmetry is not a leverage effect, and it can't be simply defined as the impact that bad news is larger than good news. And so I think volatility asymmetry should be defined as the significant difference between the impact of good news and bad one. In this article I also probe into the financial economic meanings of volatility asymmetry and find the information impact curve could be either in V shape or in S shape. And I cite some indices to measure the asymmetric degree of volatility as well.
Secondly, I examine leverage hypothesis and volatility feedback story whether explain the volatility asymmetry phenomena in the following two ways: (1) I find leverage hypothesis cann't explain why the impact of good news sometimes is larger than bad news and make empirical analysis on the section data and time series data of our stock market, and find the correlation between leverage ratios and volatility asymmetry is not significant and the precondition of leverage hypothesis is not fulfilled. And I conclude that this hypothesis can not explain the asymmetry. (2) After analyzing the logic of the volatility feedback story, I find it could not explain the asymmetry at firm level and the reason why sometimes the impact of good news is larger than bad news. I compare the asymmetric degree of high beta stocks with low beta stocks to find only in bearish market the correlation is significant and positive. And it indicates the necessary conditions of this theory can not be satisfied. Totally, this story can not explain the asymmetry of China's stock market.
Thirdly, I start my study on whether stock style, market status and character of investor influence the asymmetry and find the influence is relatively significant in the following three aspects: (1) Empirical studies show the asymmetry is positive in bullish market and negative in bearish one. By behavioral finance analysis, we find market status influences investor's mood, causes understanding of information and reacting to its irrationality and results in volatility asymmetry. And so I recommend the market status hypothesis to explain the asymmetry and support three cases to prove, such as interest rate adjustment, stock circulating institution reform, and IPO and refinance,. (2) We find the asymmetric degree of volatility of growth stocks is larger than value stocks in bullish market, but in bearish they have no significant difference. Also I have analyzed the correlation between market value and the asymmetric degree, but results show they are not significant. (3) Empirical studies indicate there is significant difference between A shares and B shares of the same company. The asymmetric degree of overweight stock of institutional investors does not differentiate with personal investors' in bullish, while the volatility and asymmetry of overweight stock of institutional investors is less than personal investors in bearish.
Finally, if considering volatility asymmetry we can estimate the statistical characteristics of stock return, forecast the volatility of stock and calculate the dynamic value at risk (VaR) accurately. Studies on the asymmetry can promote the regulator to carry out new polices and disclose new information in appropriate time, such it could enhance the stability of the market and financial efficiency of company, and improve investors to manage portfolio and risk more efficiently.
Four contributions are made in this article: (1) make a more accurate definition of volatility asymmetry. (2) firstly investigate the reasons of the asymmetry of China's stock market in several angles including leverage ratios, volatility feedback, stock style, market status and character of investors. (3) construct a new model with market status hypothesis to explain the asymmetry in behavioral finance angle. (4) discuss the meaning of volatility asymmetry to regulators, investors and listed company.
引文
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[32] Duffee, G. R. Balance sheet explanations for asymmetric volatility. Draft, May 8,2002
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[38] Eraker, B., M. Johannes, N. Polson. The impact of jumps in volatility and returns. The Journal of Finance, Vol. LVIII,No. 3, June 2003
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[40] Fornari, F. and A. Mele. Weak Convergence and Distributional Assumpions for The Asymmetric Power ARCH Model. University of Paris X, 1994, mimeo.
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[42] French Kenneth R. , G. William Schwert, and Robert F. Stambaugh. Expected stock returns and volatility. Journal of Financial Economics, 1987, 19:3～29
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[45] Glosten, L., R. Jagannathan, and D. Runkle. On the Relation Between the Expected Value and the Volatility on the Nominal Excess Returns on Stocks. Journal of Finance, 1993,48:1779-1801
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[47] Harvey, A. C. and N. Shepherd. The estimation of an asymmetric stochastic volatility model for asset returns. Journal of Business and Economic Statistics, 1996,14:429-434
[48] Hentschel, L. All in the Family Nesting Symmetric and Asymmetric GARCH Models. Journal of Financial Economics,1995, 39:71-104
[49] Heiko Ebens. Realized stock volatility. Draft, July 1999
[50] Hull, J., A. White. The pricing of options on assets with stochastic volatilities. Journal of Finance, 42:281-300
[51] Jagannathan, Ravi, ZHenyu Wang. The conditional CAPM and the Cross-section of expected returns. The Journal of Finance, Vol. LI No. 1, March 1996
[52] Koutmos G. Asymmetric volatility and risk return tradeof in foreign stock markets. Journal of Multinational Financial Management. 1992,2: 27—43
[53] Kahneman, Daniel, and Amos Tversky. Prospect Theory: An analysis of decision under risk. Econometrica, 1979, 47:263-291
[54] Lee, W. Y., C. X. Jiang, D. C. Indro. Stock market volatility, excess returns, and the role of investor sentiment. Journal of Banking & Finance, 2002, 26:2277-2299
[55] Li, George. Time-varying risk aversion and asset prices. Journal of banking & Finance, 2006
[56] Linton, Oliver and Mammen, Eric. Estimating semiparametric ARCH (∞) models by kernel smoothing methods. Econometrica, 2005, 73 (3): 771-836
[57] Litvinova, J. Volatility asymmetric in high frequency data. Draft, Duke university
[58] McKenzie, M. D., S. Kim. Evidence of an asymmetry in the relationship between volatility and autocorrelation. International Review of Financial Analysis, 2005
[59] Mehra, Sah. Mood, Projection bias and equity market volatility. Journal of dynamics and control, 2002,26:513-523
[60] Nelson , D.. ARCH Models as Diffusion Approximations. Journal of Econometrics, 1990,45: 7-38
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[62] Omori, Y. etc. Stochastic volatility with leverage: fast likelihood inference. Draft, Aug. 2004
[63] Patrick Dennis, Stewart Mayhew, Chris Stivers. Stock returns, implied volatility innovations, and the asymmetric volatility phenomenon. Draft, January 5, 2005
[64] Pietro veronesi. Stock market overreaction to bad news in good times: a rational expectations equilibrium model. The Review of Financial Studies, 1999,12:975-1007
[65] Pindyck, Robert S., Risk, inflation, and the stock market.. American Economic Review, 1984, 74:1115-1153
[66] Poon Ser—Huang, TsylorS. J. Stock returns and volatility: an empirical study of the U. K. stock market. Journal of Banking and Finance, 1992, 16: 37—59
[67] Rabemananjara, R. and J. M. Zakolin. Threshold ARCH Models and Asymmetries in Volatility. Journal of Applied Econometrics, 1993:8:31-49
[68] Renate M., J. Yu. Bugs for a Bayesian analysis of stochastic volatility models. Econommetrics Journal, 2000, 10:1-20
[69] Selcuk, F. Asymmetric stochastic volatility in emerging stock markets. Draft, 2005
[70] Schwert, G. W. .Why does stock market volatility change over time. Journal of Finance, 1989, 45:1129-1155
[71] Schwert, G. W.. Stock volatility and the crash of 87. Review of Financial Studies, 1990,3:77-102
[72]Sentana E.and Wadhwani S.Feedback traders and stock return autocorrelations:evidence from a century of daily data.Economic journal.1992,102:415-25
[73]Sharp,W..Capital asset prices:A theory of market equilibrium under conditional of risk.Journal of Finance,1964,19:425-442
[74]Shiller R J.Stock prices and social danamics.Brookings Papers on Economic Activity.1984,2:457-98
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[75]Tabak,B.M.,S.M.Guerra.Stock returns and volatility.Draft,May 15,2002
[76]Taylor S.J.,Modeling financial time series.John Wiley,Chichester,1986
[77]Thaler,Richard,ect.The effect of myopia and loss aversion on risk-taking:An experimental test.Quarterly Journal of Economics,122:647-661
[78]Wang,Kevin Q..Asset Priding with conditioning information:h new test.Rotman School of Management,University of Toronto,draft,Oct.1999
[79]Wu G..The determinants of asymmetric volatility.The Review of Financial Studies,2001,14(3):837-859
[80]Wu,G.,Z.Xiao.A generalized partially linear models of asymmetric volatility.Working paper,1999,University of Michigan
[81]Yang,J.W..The leverage effect and herding behavior in Taiwan' s stock market.Draft
[82]Yamamoto,R..What causes clusted and asymmetric volatility of stock returns?One possible Explanation.Preliminary Draft,February 1,2006
[83]Yu J..On leverage in a stochastic volatility model.Journal of Econometrics,2005,127:165-178
[84]Zakoian,J.M..Threshold Heteroskedastic Models.Journal of Economic Dynamics and Control,1994,18:931-955
[85]Zhang,X.,M.L.King.Estimation of asymmetric box-cox stochastic volatility models using MCMC simulation.Working paper,Monash University Australia,Oct.2003
[86]Andrei Shleifer著,赵英军译.并非有效的市场一行为金融学导论.北京:中国人民大学出版社,2003
[87]Chris Brooks著,邹宏元等译.金融计量经济学导论.成都:西南财经大学出版社,2005
[88]Kirill Llinski著,殷剑峰译.金融物理学.北京:机械工业出版社,2003
[89]Ruey S.Tsay著,潘家柱译.金融时间序列分析.北京:机械工业出版社,2006
[90]波涛.证券投资理论与证券投资战略适应性分析.北京:经济管理出版社,1999
[91]陈工孟、芮萌.中国股票市场的股票收益与波动关系研究,系统工程理论与实践,2003(10):12-21
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[95]范钛.中国证券市场分割对政策信息的非对称性反应研究.统计与决策,2006,(3):114-116
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