中国股票市场行业收益波动溢出效应
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摘要
波动溢出效应是金融市场主要特征之一，国内外对此研究已开展多年。从文献上看，研究者大多关注区域之间波动溢出效应的研究，较少对一个市场内部行业间的溢出效应进行实证研究。实际上，不同股票彼此之间有着密切的关系，形成特定波动传导机制，特别是在中国这样一个新兴的股票市场上，投资者受政策预期主导，决策与行为趋同，客观上强化了波动传导的动态作用机制。因此了解中国股票市场内部波动溢出效应传导机制是非常重要的。
论文第一部分为序言，论述选题的意义、概念的定义和研究目的及其文章结构；第二部分详细叙述了研究问题的国内外文献；第三部分叙述所应用的理论；
第四部分在定性和静态的情形下，应用格兰杰因果关系理论和脉冲响应函数理论分析上海股票市场中的综合指数、公用指数、商业指数、工业指数和地产指数之间的波动传导关系，结果表明：五指数之间存在着波动联动性，但其自身波动的独立性也很强，尤其是地产行业。新息的影响一般可以持续到30天左右。
第五部分在定量和动态的情形下，应用BEKK-GARCH模型实证分析五指数之间的波动溢出效应。在模型的应用中，考虑到检验模型系数的非线性约束的困难性，本文采用对矩阵元素进行似然比检验和Wald检验而不是对元素的非线性组合进行似然比检验和Wald检验。在这种假设检验下，模型可以很好的测量波动溢出效应的数量大小以及信息传递的方式。实证分析表明综合指数对其它四个指数具有单向波动溢出效应，但是溢出的数量都很小。商业指数和公用指数、工业指数和商业指数、商业指数和地产指数、地产指数和工业指数之间具有双向的波动溢出效应，其中工业指数对地产指数的波动溢出效应最大。不具有协同持续性，波动溢出风险不能够通过构建持续向量来规避。
最后一部分是结论，并应用行为金融学对波动溢出效应进行了简单的解释。
随着中国股票市场的日趋完善，股票价格对信息的反映会更迅速更准确，行业间的波动传导机制和波动溢出效应数量会出现一些变化，但是本文的研究方法还是适用的。
The volatility spillover effect is one of main characteristics in the stock market, which is researched many years in domestic and foreign. Looked from the literature, the researcher mostly pays attention to the volatility spillover between the regions and pays little attention to the volatility spillover in the market interior. In fact, there has the close relationship in the deferent stocks. The deferent stocks form the specific mechanism of the volatility spillover. Especially in Chinese emerging market investors are dominated by the government policies and prone to form an isomorphic anticipation. This strengthens the dynamic transmission mechanism of the volatility spillover. So, it is important to know the mechanism of the volatility spillover effects in the China Stock market.
First part as preface elaborates the selected topic significance, concept definition and research goal and article structure; the second narrates domestic and foreign literature about the research question; Third part introduces application theory.
Under qualitative and static situation, applied the Grange causal relation theory and the pulse response function theoretical, fourth part analysis the volatility transmission relates among the composite index, the public index, the commercial index, the industry index and the real estate index in the Shanhai stock market. The empirical study indicated: there has a remarkable volatility co-movement among the five indexes, but its own volatility independence is also strong. The influence of Innovation may last generally to 30 day about.
Under quantitative and dynamic situation, Fifth part applies multi-dimensional GARCH model and analyzes empirically volatility spillover effect among the five indexes. In the model application, tested model coefficient non-linear restraint difficult, this article carry on the likelihood ratio test and the Wald test to the matrix element and not to the element non-linear combination. Under this kind of hypothesis test, the model may estimate volatility spillover effect quantity size as well as the information transmission way. And empirical study indicated: the composite index has the one-way volatility spillover effect to other four indices, but volatility spillover quantity very is small. Between the commercial index and the public index, the industry index and the commercial index, the commercial index and the real estate index, the real estate index and the industry index have the bi-directional volatility spillover effect, in which industry index are biggest to the real estate index volatility spillover effect.
Last part is a conclusion, and the application of behavior finance has carried on the simple explanation to the volatility spillover effect.
With development of the Chinese stock market, the stock price to information reflection can be more rapid and more accurate. Although volatility transmission mechanism and volatility spillover size can appear some changes, the methods in the paper are still useful.
论文第一部分为序言，论述选题的意义、概念的定义和研究目的及其文章结构；第二部分详细叙述了研究问题的国内外文献；第三部分叙述所应用的理论；
第四部分在定性和静态的情形下，应用格兰杰因果关系理论和脉冲响应函数理论分析上海股票市场中的综合指数、公用指数、商业指数、工业指数和地产指数之间的波动传导关系，结果表明：五指数之间存在着波动联动性，但其自身波动的独立性也很强，尤其是地产行业。新息的影响一般可以持续到30天左右。
第五部分在定量和动态的情形下，应用BEKK-GARCH模型实证分析五指数之间的波动溢出效应。在模型的应用中，考虑到检验模型系数的非线性约束的困难性，本文采用对矩阵元素进行似然比检验和Wald检验而不是对元素的非线性组合进行似然比检验和Wald检验。在这种假设检验下，模型可以很好的测量波动溢出效应的数量大小以及信息传递的方式。实证分析表明综合指数对其它四个指数具有单向波动溢出效应，但是溢出的数量都很小。商业指数和公用指数、工业指数和商业指数、商业指数和地产指数、地产指数和工业指数之间具有双向的波动溢出效应，其中工业指数对地产指数的波动溢出效应最大。不具有协同持续性，波动溢出风险不能够通过构建持续向量来规避。
最后一部分是结论，并应用行为金融学对波动溢出效应进行了简单的解释。
随着中国股票市场的日趋完善，股票价格对信息的反映会更迅速更准确，行业间的波动传导机制和波动溢出效应数量会出现一些变化，但是本文的研究方法还是适用的。
The volatility spillover effect is one of main characteristics in the stock market, which is researched many years in domestic and foreign. Looked from the literature, the researcher mostly pays attention to the volatility spillover between the regions and pays little attention to the volatility spillover in the market interior. In fact, there has the close relationship in the deferent stocks. The deferent stocks form the specific mechanism of the volatility spillover. Especially in Chinese emerging market investors are dominated by the government policies and prone to form an isomorphic anticipation. This strengthens the dynamic transmission mechanism of the volatility spillover. So, it is important to know the mechanism of the volatility spillover effects in the China Stock market.
First part as preface elaborates the selected topic significance, concept definition and research goal and article structure; the second narrates domestic and foreign literature about the research question; Third part introduces application theory.
Under qualitative and static situation, applied the Grange causal relation theory and the pulse response function theoretical, fourth part analysis the volatility transmission relates among the composite index, the public index, the commercial index, the industry index and the real estate index in the Shanhai stock market. The empirical study indicated: there has a remarkable volatility co-movement among the five indexes, but its own volatility independence is also strong. The influence of Innovation may last generally to 30 day about.
Under quantitative and dynamic situation, Fifth part applies multi-dimensional GARCH model and analyzes empirically volatility spillover effect among the five indexes. In the model application, tested model coefficient non-linear restraint difficult, this article carry on the likelihood ratio test and the Wald test to the matrix element and not to the element non-linear combination. Under this kind of hypothesis test, the model may estimate volatility spillover effect quantity size as well as the information transmission way. And empirical study indicated: the composite index has the one-way volatility spillover effect to other four indices, but volatility spillover quantity very is small. Between the commercial index and the public index, the industry index and the commercial index, the commercial index and the real estate index, the real estate index and the industry index have the bi-directional volatility spillover effect, in which industry index are biggest to the real estate index volatility spillover effect.
Last part is a conclusion, and the application of behavior finance has carried on the simple explanation to the volatility spillover effect.
With development of the Chinese stock market, the stock price to information reflection can be more rapid and more accurate. Although volatility transmission mechanism and volatility spillover size can appear some changes, the methods in the paper are still useful.
引文
[1]．史树中，《金融经济学十讲》，上海人民出版社，2004
[2]．许长新．沪深联动性研究．技术经济．2001．10．
[3]. Chui, A. C. and C. Y. Kwok, 1998, Cross-autocorrelation be2tween A share and B share in Chinese stock market. Journalof Fiancial Research. 1998.
[4]．王娟花，王浣尘，引导关系实证研究—上证A股指数与上证B股指数的引导关系检验，预测，2000，(4)
[5]. Chen, G.M.; Lee, BS; Rui, O.; 2001, Foreign ownership restrictionsand market segmentation in China's stock markets; Journal of Financial Research; Vol. 24, p133-155, 23p.
[6]. Qian S., H. S Tong. The effect of market segmentation on stockpriees: The Chinese syndrome, Journal of Banking & Finance. 2000.
[7] 洪永淼等，中国股市与世界其它股市之间的大风险溢出效应，CFEF研究报告，2003．8。
[8]. Fung Hung G., W., Lee, and W. K. Leung. Segmentation ofthe A-and B-share Chinese equity markets[J], Journal of Fi2nancial Research vol. 2000.
[9]．何诚颖，中国股市“板块现象”分析，经济研究，2001．12
[10]．赵留彦、王一鸣，A、B股之间的信息流动与波动溢出，金融研究，2003．10
[11] 朱宏泉、卢祖帝，中国股市的Granger因果关系分析．管理科学学报．2001．11
[12]．俞世典，主要股票指数的联动分析，复旦计量金融网，2001
[13] 王群勇、王国忠，沪市A、B股市场间信息传递模式研究，南开经济研究，2005．6
[14]．谷耀．沪、深、港股市收益、波动溢出效应与动态相关性——基于DCC-(BV)EGARCH-VAR的检验．第二界金融年会论文．2005
[15]．汪素南等，美国股市和中国股市之间波动溢出效应实证分析，浙江大学学报，2004．11。
[16]．李双成等，中国股票市场收益和波动溢出效应分析，河北经贸大学学报，2005．9
[17]．潘慧峰等，石油市场波动溢出效应模型研究，中国软科学，2005．8
[18]．高铁梅，Eviews软件应用讲课稿．东北财经大学，2005
[44]．李子奈，高等计量经济学．清华大学出版社 2000．9
[19]．王春峰，金融市场风险管理，天津大学出版社，2000年
[20]．张世英，协整理论和波动模型，清华大学出版社，2004．9
[21]．李汉东，张世英，BEKK模型协同持续性研究，系统工程学报，2001，16(3)。
[22]．史代敏，股票市场波动的政策影响效应，管理世界，2002(8)，
[23]．李萌，叶俊．2003l．中国股票市场风险的实证分析研究，数理统计与管理．22(4)，p12-17．
[24]．张哓峒．计量经济学软件Eviews使用指南，南开大学出版社．2003．7
[25]．张晓峒，计量经济分析．经济科学出版社，2000．
[26]．古扎拉蒂，计量经济学．中国人民大学出版社，2000
[27]．耿广棋，“中国股票市场系统风险的特征与传递机制，”深圳证券交易所出版物，2002，http://www.sse.org.cn．
[28]．李国平，行为金融学，北京大学出版社，2006．1
[1]. Ross, S. A. (1989). Information and volatility: the no-arbitrate martingale approach to timing and resolution irrelevancy. Journal of Finance, 44, 1-17.
[2]. Woo, T., J. Sachs and K. Schwab, The Asian Financial Crisis: Lessons from a Resilient Asia, Cambridge: MIT Press MA, 2000.
[3]. Engle, Robert F., Takatoshi Ito, and Wen-Ling Lin, 1990, "Meteor Showers orHeat Waves? Heteroskedastic Intra Daily Volatility in the Foreign Exchange Market," Econometrica 58, 525-542.
[4]. Hamao, Y., R.W.Masulis and V.Ng. Correlation in Price Changes and Volatility across International Stock Markets. The Review of Financial Studies. 1990. 3(2):281-307
[5]. Engle, R. F. and Susmel, R. Common volatility in international equity markets. Journal of Business and Economic Statistics. 1993. 11:167-176.
[6]. Eun, C. and S. Shim. International Transmission of Stock Market Movements. Journal of Financial and Quantitative Analysis. 1989. 24: 241-256.
[7]. King.M.A. and S. Wadhwani. Transmission of Volatility Between Stock Markets. Review of Financial Studies. 1990. 3: 5-33
[8]. Lau S. T. and J. D. Diltz, (1994). Stock returns and the transfer of information Journal Of International Money and Finance,. between the New York and Tokyo stock and Finance, 211-222.
[9]. Hung, B. W. S. and Y. L. Cheung (1995). Interdependence of asian emerging equity market. Journal of Business Finance and Accounting, 22, 281-288.
[10]. Neuburger. The International Transmission of Conditional Volatility: A 21 Market Study. Fifth Midwest Econometrics Group Meetings. Washington University in St. Louis. 1995
[11]. Karolyi, G.A. A multivariate GARCH model of international transmission if stock return and volatility: the case of the United States and Canada. Journal of Business and Economics statistic, 1995, 13: 11-25.
[12]. Booth, G. G., Martikainen, T., & Tse, Y. (1997). Price and volatility spillovers in Scandinavian stock markets. Journal of Banking and Finance, 21, 811-823.
[13]. Darbar, S. M., & Deb, P. (1999). Linkages among asset markets in the United States—tests in a bivariate GARCH framework. IMF Working Paper WP/99/158.
[14]. Eduardo D. Roca, E. Antony Selvanathan and William F. Shepherd. Are the ASEAN Equity Markets Interdependent. ASEAN Economic Bulletin. 1998. 15(9).
[15]. Kanas, A. (1998). Volatility spillovers across equity markets: European evidence. Applied Financial Economics, 8, 245-256.
[16]. Darbar, S. M., & Deb, P. (2000). Transmission of information and cross-market correlations: Indiana University-Purdue University Indianapolis Working Paper.
[17]. Gokce Soydemir. International transmission mechanism of stock market movements: Evidence from emerging equity markets. Journal of Forecasting. 20
[18]. Roberto Rigobon, Measuring the Reaction of Monetary Policy to the Stock Market. NBER Working Paper 8350, 2001.
[19]. Parodi, Francisco Jose.; by, Ph.D.2002. Three essays on economic integration in Latin America (Argentina, Brazil; Mexico, Chile). Dissertation Abstracts Intemational, Volume: 63-04, Section: A, page: 1459; Advisers: Owen Beelders; Caglar Ozden.
[20]. Stokes, H.H. and Neuburger, N.M., 1998, New Methods in Financial Modeling: Explorations and Applications. Quorum Books, Westport, CT.
[21]. Ewing, Bradley T., 2002, The Transmission of Shocks Among S&P Indexes. Applied Financial Economics, 12, pp.285-290.
[22] Terrance Grieb, Mario G. Reyes, The temporal relationship between large-and small-capitalization stock retums: Evidence from the UK, Review of Financial Economics 11 (2002) 109-118.
[23] Merit, Ilhan, Meric, Gulser. Potential Gains from International Portfolio Diversification and Inter-Temporal Stability and Seasonality in International Stock Market Relationships. Journal of Banking & Finance. 1989. 13(5): 627-643.
[24]. King, Mervyn, Sentana, Enrique, Wadhwani, Sushil. Volatility and links between national stock markets. Econometrica. 1994. 62(4): 901-933
[25]. Engle, R.E, Bollerslev, T.Modeling the persistence of conditional variances, Econometric Reviews, 1986, 5: 81-97.
[2]．许长新．沪深联动性研究．技术经济．2001．10．
[3]. Chui, A. C. and C. Y. Kwok, 1998, Cross-autocorrelation be2tween A share and B share in Chinese stock market. Journalof Fiancial Research. 1998.
[4]．王娟花，王浣尘，引导关系实证研究—上证A股指数与上证B股指数的引导关系检验，预测，2000，(4)
[5]. Chen, G.M.; Lee, BS; Rui, O.; 2001, Foreign ownership restrictionsand market segmentation in China's stock markets; Journal of Financial Research; Vol. 24, p133-155, 23p.
[6]. Qian S., H. S Tong. The effect of market segmentation on stockpriees: The Chinese syndrome, Journal of Banking & Finance. 2000.
[7] 洪永淼等，中国股市与世界其它股市之间的大风险溢出效应，CFEF研究报告，2003．8。
[8]. Fung Hung G., W., Lee, and W. K. Leung. Segmentation ofthe A-and B-share Chinese equity markets[J], Journal of Fi2nancial Research vol. 2000.
[9]．何诚颖，中国股市“板块现象”分析，经济研究，2001．12
[10]．赵留彦、王一鸣，A、B股之间的信息流动与波动溢出，金融研究，2003．10
[11] 朱宏泉、卢祖帝，中国股市的Granger因果关系分析．管理科学学报．2001．11
[12]．俞世典，主要股票指数的联动分析，复旦计量金融网，2001
[13] 王群勇、王国忠，沪市A、B股市场间信息传递模式研究，南开经济研究，2005．6
[14]．谷耀．沪、深、港股市收益、波动溢出效应与动态相关性——基于DCC-(BV)EGARCH-VAR的检验．第二界金融年会论文．2005
[15]．汪素南等，美国股市和中国股市之间波动溢出效应实证分析，浙江大学学报，2004．11。
[16]．李双成等，中国股票市场收益和波动溢出效应分析，河北经贸大学学报，2005．9
[17]．潘慧峰等，石油市场波动溢出效应模型研究，中国软科学，2005．8
[18]．高铁梅，Eviews软件应用讲课稿．东北财经大学，2005
[44]．李子奈，高等计量经济学．清华大学出版社 2000．9
[19]．王春峰，金融市场风险管理，天津大学出版社，2000年
[20]．张世英，协整理论和波动模型，清华大学出版社，2004．9
[21]．李汉东，张世英，BEKK模型协同持续性研究，系统工程学报，2001，16(3)。
[22]．史代敏，股票市场波动的政策影响效应，管理世界，2002(8)，
[23]．李萌，叶俊．2003l．中国股票市场风险的实证分析研究，数理统计与管理．22(4)，p12-17．
[24]．张哓峒．计量经济学软件Eviews使用指南，南开大学出版社．2003．7
[25]．张晓峒，计量经济分析．经济科学出版社，2000．
[26]．古扎拉蒂，计量经济学．中国人民大学出版社，2000
[27]．耿广棋，“中国股票市场系统风险的特征与传递机制，”深圳证券交易所出版物，2002，http://www.sse.org.cn．
[28]．李国平，行为金融学，北京大学出版社，2006．1
[1]. Ross, S. A. (1989). Information and volatility: the no-arbitrate martingale approach to timing and resolution irrelevancy. Journal of Finance, 44, 1-17.
[2]. Woo, T., J. Sachs and K. Schwab, The Asian Financial Crisis: Lessons from a Resilient Asia, Cambridge: MIT Press MA, 2000.
[3]. Engle, Robert F., Takatoshi Ito, and Wen-Ling Lin, 1990, "Meteor Showers orHeat Waves? Heteroskedastic Intra Daily Volatility in the Foreign Exchange Market," Econometrica 58, 525-542.
[4]. Hamao, Y., R.W.Masulis and V.Ng. Correlation in Price Changes and Volatility across International Stock Markets. The Review of Financial Studies. 1990. 3(2):281-307
[5]. Engle, R. F. and Susmel, R. Common volatility in international equity markets. Journal of Business and Economic Statistics. 1993. 11:167-176.
[6]. Eun, C. and S. Shim. International Transmission of Stock Market Movements. Journal of Financial and Quantitative Analysis. 1989. 24: 241-256.
[7]. King.M.A. and S. Wadhwani. Transmission of Volatility Between Stock Markets. Review of Financial Studies. 1990. 3: 5-33
[8]. Lau S. T. and J. D. Diltz, (1994). Stock returns and the transfer of information Journal Of International Money and Finance,. between the New York and Tokyo stock and Finance, 211-222.
[9]. Hung, B. W. S. and Y. L. Cheung (1995). Interdependence of asian emerging equity market. Journal of Business Finance and Accounting, 22, 281-288.
[10]. Neuburger. The International Transmission of Conditional Volatility: A 21 Market Study. Fifth Midwest Econometrics Group Meetings. Washington University in St. Louis. 1995
[11]. Karolyi, G.A. A multivariate GARCH model of international transmission if stock return and volatility: the case of the United States and Canada. Journal of Business and Economics statistic, 1995, 13: 11-25.
[12]. Booth, G. G., Martikainen, T., & Tse, Y. (1997). Price and volatility spillovers in Scandinavian stock markets. Journal of Banking and Finance, 21, 811-823.
[13]. Darbar, S. M., & Deb, P. (1999). Linkages among asset markets in the United States—tests in a bivariate GARCH framework. IMF Working Paper WP/99/158.
[14]. Eduardo D. Roca, E. Antony Selvanathan and William F. Shepherd. Are the ASEAN Equity Markets Interdependent. ASEAN Economic Bulletin. 1998. 15(9).
[15]. Kanas, A. (1998). Volatility spillovers across equity markets: European evidence. Applied Financial Economics, 8, 245-256.
[16]. Darbar, S. M., & Deb, P. (2000). Transmission of information and cross-market correlations: Indiana University-Purdue University Indianapolis Working Paper.
[17]. Gokce Soydemir. International transmission mechanism of stock market movements: Evidence from emerging equity markets. Journal of Forecasting. 20
[18]. Roberto Rigobon, Measuring the Reaction of Monetary Policy to the Stock Market. NBER Working Paper 8350, 2001.
[19]. Parodi, Francisco Jose.; by, Ph.D.2002. Three essays on economic integration in Latin America (Argentina, Brazil; Mexico, Chile). Dissertation Abstracts Intemational, Volume: 63-04, Section: A, page: 1459; Advisers: Owen Beelders; Caglar Ozden.
[20]. Stokes, H.H. and Neuburger, N.M., 1998, New Methods in Financial Modeling: Explorations and Applications. Quorum Books, Westport, CT.
[21]. Ewing, Bradley T., 2002, The Transmission of Shocks Among S&P Indexes. Applied Financial Economics, 12, pp.285-290.
[22] Terrance Grieb, Mario G. Reyes, The temporal relationship between large-and small-capitalization stock retums: Evidence from the UK, Review of Financial Economics 11 (2002) 109-118.
[23] Merit, Ilhan, Meric, Gulser. Potential Gains from International Portfolio Diversification and Inter-Temporal Stability and Seasonality in International Stock Market Relationships. Journal of Banking & Finance. 1989. 13(5): 627-643.
[24]. King, Mervyn, Sentana, Enrique, Wadhwani, Sushil. Volatility and links between national stock markets. Econometrica. 1994. 62(4): 901-933
[25]. Engle, R.E, Bollerslev, T.Modeling the persistence of conditional variances, Econometric Reviews, 1986, 5: 81-97.