我国股市波动中的机制转换行为研究
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摘要
中国股市从诞生到成熟已经过20年的发展历程,20年来我国股市始终保持着较高的波动水平,不仅加大了股市的系统风险,而且一定程度上影响了正常的金融秩序。我国股市波动通常呈现出高、低波动状态交替出现的特点,表现为股市波动的机制转换行为。因此,研究我国股市波动的机制转换行为及其影响因素对于分析和把握股市波动特征,平抑股市剧烈波动,促进股市健康发展具有重要意义。
本文选用TAR模型、MSA模型和SWARCH模型这三种基本的机制转换模型就我国股市波动的机制转换行为展开分析。首先,本文将我国股市波动情况与世界主要股指进行了比较,并分析了我国股市波动的特征。其次,通过介绍TAR模型、MSA模型和SWARCH模型这三种基本的机制转换模型对他们的基本原理、假设条件等方面在理论上进行了比较,说明了这些模型各自的优缺点以及适用性。最后,将这三种模型用于上证综指的周收益率的研究中,利用实证结果从收益率和方差两个层面对股市进行分析并认为:收益率自身存在一种自我修正机制;收益率和方差之间也存在一种相互适应的关系;MSA模型的状态转换体现的是短期因素的影响,而SWARCH模型的状态转换体现的是长期因素的影响,并据此分析了影响我国股市波动机制转换行为的长、短期因素。综合上述结论,文章对平抑股市剧烈波动提出对策建议。
It has been 20 years since the Chinese stock market was born, and the Chinese stock market has been keeping a high volatility level for these 20 years. It doesn’t only augment the stock market’s system risk, but also disturbs the normal financial order to some degree. Our stock market’s volatility usually shows the high volatility state and the low volatility state emerging alternately, that is the regime switching of stock market’s volatility. Therefore, studying the regime switching of our stock market’s volatility and the switching factors has its own significance for analyzing the character of our stock market’s volatility, weakening the violent stock market’s volatility and promoting our stock market’s healthy development.
We apply TAR model, MSA model and SWARCH model, these three basic regime switching models, to the research about the regime switching of our stock market’s volatility. Firstly, we put our stock market’s volatility and main stock market indexes into contrast and point the character of our stock market’s volatility. Secondly, we introduce TAR model, MSA model and SWARCH model, and by theoretically comparing these three basic regime switching models on basic principle, hypothesis and other aspects, we analyze the merits and drawbacks, also the applicability of each model. At last, applying these three models to the analysis of the weekly returns of shanghai composite index, with respect of the returns and the variance, we get the positive results which show that: the returns itself has a self-correcting regime; there is a reciprocal adaptation relationship between the returns and the variance; MSA model embodies the effect of short term factors while the SWARCH model embodies the effect of long term factors, and on such a base, we analyze the long and short term factors that affect the regime switching of our stock mark’s volatility. With all the results above, we put forward some advice on how to weaken our stock market’s violent volatility.
本文选用TAR模型、MSA模型和SWARCH模型这三种基本的机制转换模型就我国股市波动的机制转换行为展开分析。首先,本文将我国股市波动情况与世界主要股指进行了比较,并分析了我国股市波动的特征。其次,通过介绍TAR模型、MSA模型和SWARCH模型这三种基本的机制转换模型对他们的基本原理、假设条件等方面在理论上进行了比较,说明了这些模型各自的优缺点以及适用性。最后,将这三种模型用于上证综指的周收益率的研究中,利用实证结果从收益率和方差两个层面对股市进行分析并认为:收益率自身存在一种自我修正机制;收益率和方差之间也存在一种相互适应的关系;MSA模型的状态转换体现的是短期因素的影响,而SWARCH模型的状态转换体现的是长期因素的影响,并据此分析了影响我国股市波动机制转换行为的长、短期因素。综合上述结论,文章对平抑股市剧烈波动提出对策建议。
It has been 20 years since the Chinese stock market was born, and the Chinese stock market has been keeping a high volatility level for these 20 years. It doesn’t only augment the stock market’s system risk, but also disturbs the normal financial order to some degree. Our stock market’s volatility usually shows the high volatility state and the low volatility state emerging alternately, that is the regime switching of stock market’s volatility. Therefore, studying the regime switching of our stock market’s volatility and the switching factors has its own significance for analyzing the character of our stock market’s volatility, weakening the violent stock market’s volatility and promoting our stock market’s healthy development.
We apply TAR model, MSA model and SWARCH model, these three basic regime switching models, to the research about the regime switching of our stock market’s volatility. Firstly, we put our stock market’s volatility and main stock market indexes into contrast and point the character of our stock market’s volatility. Secondly, we introduce TAR model, MSA model and SWARCH model, and by theoretically comparing these three basic regime switching models on basic principle, hypothesis and other aspects, we analyze the merits and drawbacks, also the applicability of each model. At last, applying these three models to the analysis of the weekly returns of shanghai composite index, with respect of the returns and the variance, we get the positive results which show that: the returns itself has a self-correcting regime; there is a reciprocal adaptation relationship between the returns and the variance; MSA model embodies the effect of short term factors while the SWARCH model embodies the effect of long term factors, and on such a base, we analyze the long and short term factors that affect the regime switching of our stock mark’s volatility. With all the results above, we put forward some advice on how to weaken our stock market’s violent volatility.
引文
[1] Yasushi Hamao, Ronald W. Masulis and Victor Ng. Correlations in Price Changes and Volatility Across International Stock Markets[J]. Review of Financial Studies. 1990,(2):281-307.
[2] Rabemananjara R, Zakoian J M. Threshold Arch Models and Asymmetries in Volatility[J]. Journal of Applied Econometrics. 1993,(1):31-49.
[3] Hamilton J.D. A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle [J]. Econometrica,1989,(57).:357-384
[4] Hamilton J.D. Analysis of Time Series Subject to Changes in Regime [J]. Journal of Econometrics, 1990,(45):39-70
[5] Hamilton J.D.and R. Susmel. Autoressivee conditional heteroseedastieity and ehanges in regime[J].Joumal of Eeonometries, 1994,(64):307-333.
[6] Cai, J. A Markov Model of Switching-Regime ARCH [J]. Journal of Business and Economic Statistics. 1994,(12):309-316.
[7] Huntley Schaller, Simon Van Norden. Regime switching in stock market returns[J]. Applied Financial Economics. 1997, (2):91-177.
[8] Timotheos Angelidis, Alexandros Benos, Stavros Degiannakis. A robust VaR model under different time periods and weighting schemes[J]. Review of Quantitative Finance and Accounting,.2007,(2):187-201.
[9] Jan S. Hennekeab, Svetlozar T. Rachevac, Frank J. Fabozzid, Metodi Nikolove. MCMC-based estimation of Markov Switching ARMA-GARCH models[J]. Applied Economics. 2011,(3):259-271.
[10] Maheu,J.M. and T.H.Mc Curdy. Identifying Bull and Bear Markets in Stock Returns [J]. Journal of Business and Economic Statistics. 2000,(18):100–11.
[11] Ruey S Tsay. Testing acid modeling threshold autoregressive processes [J] . Journal ofthe American Statistical Association. 1989,(84):231 - 240.
[12] Tong H. Threshold models in nonlinear time series analysis [M]. New York :Springer Verlag : 1983.
[13] Diebold, F.X.,and A.Inoue. Long Memory and Regime Switching[J]. Journal of Econometrics. 2001,(105):131–159.
[14] Bollen,N.P.B. ,S.F.Gray,and R.E.Whaley. Regime Switching in Foreign Exchange Rates:Evidence from Currency Option Prices[J]. Journal of Econometrics. 2000, (94): 239–276.
[15] Engel, C., and J.D. Hamilton. Long Swings in the Dollar:Are they in the Data and do Markets know it [J] American Economic Review. 1990,(80) :689–713.
[16] Brunetti, C.,R.S. Mariano, C.Scotti, and A.H.H.Tan. Markov Switching GARCH Models of Currency Crises in Southeast Asia[J]. Working Paper 03-008, Penn Institute for Economic Research.2003
[17] Granger, C.W.J and T. Terasvirta. A Simple Nonlinear Time Series Model with Misleading Linear Properties[J].Econometrics Letters. 1999,(62) :161-165.
[18] MeCulloch,R.E and R.S.Tsar. Statistical analysis of economic time series via Markov switching models[J]. Journal of Time Series Analysis.1994,(15) :523一539.
[19] Dewachter,H. Can Markov Switching Models Replicate Chartist Profits in the Foreign Exchange Market[J]. Journal of Internationa1 Money and Finance. 2001,(20) :25-41.
[20]赵留彦,王一鸣.沪深股市交易量与收益率及其波动的相关性——来自实证分析的证据[J].经济科学. 2003, (2).
[21]龚锐,陈仲常,杨栋锐. GARCH族模型计算中国股市在险价值(VaR)风险的比较研究与评述[J].数量经济技术经济研究, 2005,(7).
[22]靳晓婷,张晓峒,栾惠德.汇改后人民币汇率波动的非线性特征研究——基于门限自回归TAR模型[J].财经研究. 2008,(9).
[23]张维,张小涛,熊熊.上海股票市场波动不对称性研究——GJR与VS-GARCH模型的比较[J];数理统计与管理. 2005,(6).
[24]蒋祥林,王春峰,吴晓霖.基于状态转移ARCH模型的中国股市波动性研究[J].系统工程学报. 2004,(3).
[25]苏涛,詹原瑞. SWARCH模型下的VaR估计[J].数量经济技术经济研究.2005,(12).
[26]苗苗,洪潇,付立新.基于Markov转移模型的中国股市状态转换研究[J].统计与决策. 2010,(12).
[27]孟庆斌,周爱民,靳晓婷.基于TAR模型的中国股市价格泡沫检验[J].南开经济研究. 2008,(4).
[27]魏巍贤,陈智文,王建军.三状态马尔柯夫机制转换模型研究——在世界油价波动分析中的应用[J].财经研究. 2006 (6).
[28]王建军. Markov机制转换模型研究——在中国宏观经济周期分析中的应用[J].数量经济技术经济研究. 2007,(3).
[29]孙金丽,张世英.具有结构转换的GARCH模型及其在中国股市中的应用[J].系统工程.2003,(6)
[30]刘霞辉.为什么中国经济不是过冷就是过热[J]?经济研究. 2004,(11).
[31]陶亮.人民币汇率的MS-GARCH模型分析[D].华中科技大学硕士学位论文,2006.
[32]赵鹏,曾剑云.我国股市周期性破灭型投机泡沫实证研究——基于马尔可夫区制转换方法.金融研究[J]. 2005,(4).
[33]万军,刘思峰,许海靖.基于状态转换GARCH模型的上证综指已实现波动研究[J].工业技术经济.2008,(4).
[34]徐信忠,黄张凯,刘寅等.大宗股权定价的实证检验[J].经济研究.2006,(l).
[35]宋健,股权分置改革对股票二级市场的影响lJ].统计与咨询.2005,(6)
[36]屈波.“大小非”减持下市场估值体系的困惑[J].证券市场.2008,(10).
[37]任梦,陈玉新.流通股比例对股票价格影响的实证研究[J].辽宁石油化工大学学报.2008,(3).
[38]刘纪鹏.中国资本市场的“后股权分置时代”[J].经济导刊.2005,(10).
[39]冯玲,限售股流通与股价效应关系的实证研究[J].技术经济.2008,(9)
[40]何诚颖,李翔.股权分置改革、扩容预期及其市场反应的实证研究[J].金融研究.2007,(4).
[2] Rabemananjara R, Zakoian J M. Threshold Arch Models and Asymmetries in Volatility[J]. Journal of Applied Econometrics. 1993,(1):31-49.
[3] Hamilton J.D. A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle [J]. Econometrica,1989,(57).:357-384
[4] Hamilton J.D. Analysis of Time Series Subject to Changes in Regime [J]. Journal of Econometrics, 1990,(45):39-70
[5] Hamilton J.D.and R. Susmel. Autoressivee conditional heteroseedastieity and ehanges in regime[J].Joumal of Eeonometries, 1994,(64):307-333.
[6] Cai, J. A Markov Model of Switching-Regime ARCH [J]. Journal of Business and Economic Statistics. 1994,(12):309-316.
[7] Huntley Schaller, Simon Van Norden. Regime switching in stock market returns[J]. Applied Financial Economics. 1997, (2):91-177.
[8] Timotheos Angelidis, Alexandros Benos, Stavros Degiannakis. A robust VaR model under different time periods and weighting schemes[J]. Review of Quantitative Finance and Accounting,.2007,(2):187-201.
[9] Jan S. Hennekeab, Svetlozar T. Rachevac, Frank J. Fabozzid, Metodi Nikolove. MCMC-based estimation of Markov Switching ARMA-GARCH models[J]. Applied Economics. 2011,(3):259-271.
[10] Maheu,J.M. and T.H.Mc Curdy. Identifying Bull and Bear Markets in Stock Returns [J]. Journal of Business and Economic Statistics. 2000,(18):100–11.
[11] Ruey S Tsay. Testing acid modeling threshold autoregressive processes [J] . Journal ofthe American Statistical Association. 1989,(84):231 - 240.
[12] Tong H. Threshold models in nonlinear time series analysis [M]. New York :Springer Verlag : 1983.
[13] Diebold, F.X.,and A.Inoue. Long Memory and Regime Switching[J]. Journal of Econometrics. 2001,(105):131–159.
[14] Bollen,N.P.B. ,S.F.Gray,and R.E.Whaley. Regime Switching in Foreign Exchange Rates:Evidence from Currency Option Prices[J]. Journal of Econometrics. 2000, (94): 239–276.
[15] Engel, C., and J.D. Hamilton. Long Swings in the Dollar:Are they in the Data and do Markets know it [J] American Economic Review. 1990,(80) :689–713.
[16] Brunetti, C.,R.S. Mariano, C.Scotti, and A.H.H.Tan. Markov Switching GARCH Models of Currency Crises in Southeast Asia[J]. Working Paper 03-008, Penn Institute for Economic Research.2003
[17] Granger, C.W.J and T. Terasvirta. A Simple Nonlinear Time Series Model with Misleading Linear Properties[J].Econometrics Letters. 1999,(62) :161-165.
[18] MeCulloch,R.E and R.S.Tsar. Statistical analysis of economic time series via Markov switching models[J]. Journal of Time Series Analysis.1994,(15) :523一539.
[19] Dewachter,H. Can Markov Switching Models Replicate Chartist Profits in the Foreign Exchange Market[J]. Journal of Internationa1 Money and Finance. 2001,(20) :25-41.
[20]赵留彦,王一鸣.沪深股市交易量与收益率及其波动的相关性——来自实证分析的证据[J].经济科学. 2003, (2).
[21]龚锐,陈仲常,杨栋锐. GARCH族模型计算中国股市在险价值(VaR)风险的比较研究与评述[J].数量经济技术经济研究, 2005,(7).
[22]靳晓婷,张晓峒,栾惠德.汇改后人民币汇率波动的非线性特征研究——基于门限自回归TAR模型[J].财经研究. 2008,(9).
[23]张维,张小涛,熊熊.上海股票市场波动不对称性研究——GJR与VS-GARCH模型的比较[J];数理统计与管理. 2005,(6).
[24]蒋祥林,王春峰,吴晓霖.基于状态转移ARCH模型的中国股市波动性研究[J].系统工程学报. 2004,(3).
[25]苏涛,詹原瑞. SWARCH模型下的VaR估计[J].数量经济技术经济研究.2005,(12).
[26]苗苗,洪潇,付立新.基于Markov转移模型的中国股市状态转换研究[J].统计与决策. 2010,(12).
[27]孟庆斌,周爱民,靳晓婷.基于TAR模型的中国股市价格泡沫检验[J].南开经济研究. 2008,(4).
[27]魏巍贤,陈智文,王建军.三状态马尔柯夫机制转换模型研究——在世界油价波动分析中的应用[J].财经研究. 2006 (6).
[28]王建军. Markov机制转换模型研究——在中国宏观经济周期分析中的应用[J].数量经济技术经济研究. 2007,(3).
[29]孙金丽,张世英.具有结构转换的GARCH模型及其在中国股市中的应用[J].系统工程.2003,(6)
[30]刘霞辉.为什么中国经济不是过冷就是过热[J]?经济研究. 2004,(11).
[31]陶亮.人民币汇率的MS-GARCH模型分析[D].华中科技大学硕士学位论文,2006.
[32]赵鹏,曾剑云.我国股市周期性破灭型投机泡沫实证研究——基于马尔可夫区制转换方法.金融研究[J]. 2005,(4).
[33]万军,刘思峰,许海靖.基于状态转换GARCH模型的上证综指已实现波动研究[J].工业技术经济.2008,(4).
[34]徐信忠,黄张凯,刘寅等.大宗股权定价的实证检验[J].经济研究.2006,(l).
[35]宋健,股权分置改革对股票二级市场的影响lJ].统计与咨询.2005,(6)
[36]屈波.“大小非”减持下市场估值体系的困惑[J].证券市场.2008,(10).
[37]任梦,陈玉新.流通股比例对股票价格影响的实证研究[J].辽宁石油化工大学学报.2008,(3).
[38]刘纪鹏.中国资本市场的“后股权分置时代”[J].经济导刊.2005,(10).
[39]冯玲,限售股流通与股价效应关系的实证研究[J].技术经济.2008,(9)
[40]何诚颖,李翔.股权分置改革、扩容预期及其市场反应的实证研究[J].金融研究.2007,(4).
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