广义事件窗中上市公司风险定价研究
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摘要
广义事件窗中的上市公司风险资产定价问题一直是资本市场中最受关注的核心问题。科学合理地分析广义事件窗中的上市公司股票对数收益水平和风险水平,为投资者对上市公司进行适当的投资选择提供了参考。目前对上市公司风险资产定价的评价通常是用一些常规方法进行评价,并且通常是在有效市场的假设下进行的,所以当金融事件或突发事件发生时,难以刻画和描述金融事件发生前后某一段时间区域中的上市公司风险水平与收益水平,因而不能客观而全面的反映在广义事件窗中的上市公司的风险资产定价问题。针对这些不足,本文试图提出一种新的理论思想,来对广义事件窗中的上市公司的风险与收益进行评价。此外,资产定价是金融经济学的核心问题。国外的资产定价理论和实证发展都比较完善,己经形成体系。本文的目的是在借鉴国外理论体系的基础上,试图对资产定价理论体系中的资产定价模型进行总结,并采用中国国内的股票数据对股票市场进行检验得出实证结果,进而加深对资产定价理论的认识,使其应用于中国股票市场。
本文是在广义事件窗中选取两个具有代表性上市公司作为样本,一个是鞍钢股份有限公司,另一个是中国石化股份有限公司。文章的主要内容分为四部分,第一部分介绍了课题的研究背景和国内外现状;第二部分阐述了本文所需要用到的一些金融时间序列分析的各种模型,尤其是GARCH类模型,为本文的分析做好铺垫;第三部分对鞍钢股份公司在整体上市前后和中国石化在股权分置改革前后的股票对数收益率序列建立了合适的GARCH模型,将他们的风险水平的变化情况作了比较研究并得到两个结果:鞍钢股份公司风险水平和收益水平都有所提高,但收益水平提高的幅度较大,这说明鞍钢股份公司实行整体上市改革后其股票的风险与收益变化合乎股市的一般规律;中国石化公司风险水平和收益水平都有所降低,但风险水平降低的幅度大一些,这说明中国石化完成股改的时间较短,股改还不成熟,有待进一步完善;第四部分利用资本资产定价模型对我国上海股票市场进行实证研究。研究结果表明,上海股市仍然是一个不成熟的股市且上海股市的投资风险—收益关系不是很符合CAPM模型的结论。
Risk asset pricing of listed companies in the wide event window has always been the most attended issue in capital market. A scientific and reasonable evaluation of the log return of stock and risk level of listed companies in the wide event window will provide reference for investors to make a proper choice of the listed company. Current theory and method are used to evaluate risk asset pricing for listed companies in a general way, on the premise that the capital market is effective. Therefore, when a financial event or an unexpected event happens, here rises a new problem, that is, there exists a obstacle to evaluate and describe the risk and return level for listed companies during a period before and after the financial event. Thus, it is difficult to evaluate risk asset pricing for listed companies in the wide event window. Therefore, this paper develops a new theoretical idea of evaluating risk and return level of listed companies in the wide event window in order to solve above problems. Furthermore, asset pricing is the core of Financial Economics. In developed countries, asset pricing theory and empirical tests have become a well-developed system. The purpose of this paper is according to the foundation of using overseas theory system to try to constract asset pricing models and deriving some useful results by using the data from Chinese stock market. Thus we can know the asset pricing models more and apply them to else stock market.
This paper selected two representative corporations in the wide event window, one is Angang corporation, the other is China petrifaction corporation .The contents of this paper could be divided into four parts. Firstly, we introduced current situation of the reform of stock and actualities of China and oversea, Secondly, we gave knowledge about the models widely used in Analysis of Financial Time Series, especially GARCH models, which paved way for further study; Thirdly, we respectively set up adequate GARCH models for the log return series before and after overall listing of Angang corporation and before and after corporations’reformation of China petrifaction. And made a comparison on the average risk level between the two periods, we conclude the risk and return levels of Angang corporation have a great extent increase, but return level of log return of stock is higher than risk level, this explains Angang overall listing reformation plan is more reasonable in the stock market. However, the risk and return levels of China petrifaction corporation have a great extent decrease, but risk level declines more great, this explains China petrifaction corporation dues to complete reformation time is short, reformation is not mature, that needs more perfect. Lastly, we employed empirical test of CAPM in China Shanghai’s stock market. We conclude that Shanghai stock market is not mature and the relation between investment risk and return of Shanghai stock market falls short of CAPM conclusions。
本文是在广义事件窗中选取两个具有代表性上市公司作为样本,一个是鞍钢股份有限公司,另一个是中国石化股份有限公司。文章的主要内容分为四部分,第一部分介绍了课题的研究背景和国内外现状;第二部分阐述了本文所需要用到的一些金融时间序列分析的各种模型,尤其是GARCH类模型,为本文的分析做好铺垫;第三部分对鞍钢股份公司在整体上市前后和中国石化在股权分置改革前后的股票对数收益率序列建立了合适的GARCH模型,将他们的风险水平的变化情况作了比较研究并得到两个结果:鞍钢股份公司风险水平和收益水平都有所提高,但收益水平提高的幅度较大,这说明鞍钢股份公司实行整体上市改革后其股票的风险与收益变化合乎股市的一般规律;中国石化公司风险水平和收益水平都有所降低,但风险水平降低的幅度大一些,这说明中国石化完成股改的时间较短,股改还不成熟,有待进一步完善;第四部分利用资本资产定价模型对我国上海股票市场进行实证研究。研究结果表明,上海股市仍然是一个不成熟的股市且上海股市的投资风险—收益关系不是很符合CAPM模型的结论。
Risk asset pricing of listed companies in the wide event window has always been the most attended issue in capital market. A scientific and reasonable evaluation of the log return of stock and risk level of listed companies in the wide event window will provide reference for investors to make a proper choice of the listed company. Current theory and method are used to evaluate risk asset pricing for listed companies in a general way, on the premise that the capital market is effective. Therefore, when a financial event or an unexpected event happens, here rises a new problem, that is, there exists a obstacle to evaluate and describe the risk and return level for listed companies during a period before and after the financial event. Thus, it is difficult to evaluate risk asset pricing for listed companies in the wide event window. Therefore, this paper develops a new theoretical idea of evaluating risk and return level of listed companies in the wide event window in order to solve above problems. Furthermore, asset pricing is the core of Financial Economics. In developed countries, asset pricing theory and empirical tests have become a well-developed system. The purpose of this paper is according to the foundation of using overseas theory system to try to constract asset pricing models and deriving some useful results by using the data from Chinese stock market. Thus we can know the asset pricing models more and apply them to else stock market.
This paper selected two representative corporations in the wide event window, one is Angang corporation, the other is China petrifaction corporation .The contents of this paper could be divided into four parts. Firstly, we introduced current situation of the reform of stock and actualities of China and oversea, Secondly, we gave knowledge about the models widely used in Analysis of Financial Time Series, especially GARCH models, which paved way for further study; Thirdly, we respectively set up adequate GARCH models for the log return series before and after overall listing of Angang corporation and before and after corporations’reformation of China petrifaction. And made a comparison on the average risk level between the two periods, we conclude the risk and return levels of Angang corporation have a great extent increase, but return level of log return of stock is higher than risk level, this explains Angang overall listing reformation plan is more reasonable in the stock market. However, the risk and return levels of China petrifaction corporation have a great extent decrease, but risk level declines more great, this explains China petrifaction corporation dues to complete reformation time is short, reformation is not mature, that needs more perfect. Lastly, we employed empirical test of CAPM in China Shanghai’s stock market. We conclude that Shanghai stock market is not mature and the relation between investment risk and return of Shanghai stock market falls short of CAPM conclusions。
引文
1叶中行,林建忠.数理金融.科学出版社, 1998
2郭多祚.数理金融.清华大学出版社, 2006
3 Javiera Aguilar, Stefan Nydahl. Central Bank Intervention and Exchange Rates: the Case of Sweden. Journal of International Financial Markets, Institutions and Money. 2000, (10) :303~322
4田波平.基于广义事件窗的上市公司风险与绩效评价.哈尔滨工业大学管理学院技术经济及管理专业博士论文. 2006:28~32
5孟庆顺.金融资产定价理论的历史回顾与展望.时代经贸. 2006, 4(8):70~72
6伍海华.随机过程―金融资产定价之应用.中国金融出版社, 2002
7 T. Bollerslev. Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics. 1986, 31:307~327
8 Duffie著,潘存武译.动态资产定价理论.上海财经大学出版社, 2003
9冯建芬,陈典发.风险资产的一般定价模型.南开大学学报. 2006
10王振龙.时间序列分析.中国统计出版社, 2003
11王军波,邓述慧.利率、成交量对股价波动的影响――GARCH修正模型的应用.系统工程理论与实践. 1999, (9):49~57
12刘继春.多维GARCH模型的估计和检验.吉林大学自然科学学报. 2000, (4): 56~40
13殷玲,唐杰. GARCH-M模型与我国沪深股市的波动.江南大学学报. 2002, (4): 56~58
14李亚静,朱宏泉,彭育威.基于GARCH模型族的中国股市波动性预测.数学的实践与认识. 2003, (11): 65~71
15惠晓峰,柳鸿生,胡伟等.基于时间序列GARCH模型的人民币汇率预测.金融研究. 2003, (5):3~8
16卢志红,郑丕谔. GARCH族模型在上海股市分阶段对比分析中的应用.中国计量学院学报. 2004, 15(1):58~61
17徐绪松,马莉莉,陈彦斌.我国上海股票市场GARCH效应实证研究.武汉大学学报(理学版). 2002, 48(3):293~296
18唐齐鸣,陈健.中国股市的ARCH效应分析.世界经济. 2001, (3): 29~36
19岳朝龙.上海股市收益率GARCH模型族的实证研究.数量经济技术研究.2001, (6):126~130
20徐剑刚,唐国兴.我国股票市场报酬与波动的GARCH-M模型.数量经济技术经济研究. 1997, 12(12):28~32
21 R. F. Engle, D. M. Lilien and R. P. Robins. Estimating Time-varying Risk Premia in the Term Structure: The ARCH-M Model. Econometrics. 1987, 55: 391~407
22 C. Cao and R. S. Tsay. Nonlinear Time Series Analysis of Stock Volatilities. Journal of Applied Econometrics. 1992, (7):165~185
23 Y. K. Tsey. A Test for Constant Correlations in A Multivariate GARCH Model. Journal of Econometrics. 2000, 98:107~127
24 Istvan. Berkes, Lajos. Horvath, Piotr. Kokoszka. Testing for Parameter Constancy in GARCH (p,q) Models. Statistics & Probability Letters. 2004, 70:263~273
25 Chris. Brooks, P. Simon. Burke, Gita. Persand. Benchmarks and Accuracy of GARCH Model Estimation. International Journal of Forecasting. 2001, 17:45~46
26 R. S. Tsay, D. Pena and A. Pankratz. Outliers in Multivariate Time Series. Biometrika. 2000, 87:789~804
27 R. S. Tsay. Nonlinearity Tests for Time Series. Biometrika. 1996, 73:461~466
28 R. S. Tsay. Analysis of Financial Time Series. Wiley. 2000
29 E. P. Box. Time Series Analysis: Forecasting and Control. Prentice Hall/Pearson. 2005
30 R. S. Tsay. Analysis of Financial Times Series. John Wiley &Sons. Inc. 2002: 20~80
31王耀东,张德远,张海雄.经济时间序列分析.上海财经大学出版社, 1996
32 R. S. Tsay著,潘家柱译.金融时间序列分析.机械工业出版社, 2006
33陈浪南,屈文洲.资本资产定价模型.经济研究. 2000, (4):26~34
34 A. K. Bera and M. L. Higgins. A Test for Conditional Heteroskedasticity in Time Series Models. Journal of Time Series Analysis. 1992, 13:501~519
35 M. L. Higgins, A. K. Bera. A Class of Nonlinear ARCH Models. International Economic Review. 1992, 33:137~158
36 C. J. Ma, M. M. Chen. ARCH Model and Its Application in Finance System. Journal of Hunan University. 1998, 25(5):108~112
37 T. Bollerslev, R. T. Chou, K. F. Kroner. ARCH Modeling in Finance. Journal of Econometrics. 1992, 52:1~59
38 K. P. Mike. So, L. H. Philip. Yub. Empirical Analysis of GARCH Models inValue at Risk Estimation. Int. Fin. Markets Inst. and Money. 2006, (16):180~197
39 Eric. Hillebrand. Neglecting Parameter Changes in GARCH Models. Journal of Econometrics. 2005, 129:121~138
40安鸿志等.时间序列的分析与应用.科学出版社, 1986
41 Teruo. Nakatsuma. Bayesian of Analysis of ARMA-GARCH Models: A Markov Chain Sampling Approach. Journal of Econometrics. 2000, 95:57~69
42 Michael. Sabatini, Oliver. Linton. A GARCH Model of the Implied Volatility of the Swiss Market Index from Option Prices. International Journal of Forecasting. 1998, 14:199~213
43施东晖.上海股票市场风险性研究.经济研究. 1996, (10):35~41
44朱鸣雄.动态资产定价理论中风险估计及其统计性质分析统计研究. 1999, (4):43~46
45李子奈,叶阿忠.高等计量经济学.清华大学出版社, 2000
46陈彦斌.基于财富偏好和习惯形成的资本资产定价模型.武汉大学管理学博士论文. 2003:46~52
47孟庆顺.资产定价模型及其在中国股票市场的检验.吉林大学管理学博士学位论文. 2005:85~94
48孙刚.资本资产定价模型在中国股票市场的实证研究.统计与信息论坛. 2003, (5):91~94
49李和金,李湛.上海股票市场资本资产定价模型实证检验.理论与方法研究. 2000, (5):75~77
50许涤龙,张钰.资本资产定价模型与上海股票市场的实证分析.大学南昌学报(理学版). 2005, (2):142~145
51易丹辉.数据分析与Eviews应用.中国统计出版社, 2002
52周少甫,陈千里.中国股市收益波动的实证研究.华中科技大学学报[J]. 2002, 30(9):48~50
53魏娜. ARCH族模型及其对深圳股票市场的实证分析.数学理论与应用. 2006, (3):60~63
54马静如.资本资产定价模型与深圳股票市场的实证研究.南开经济研究. 2001, (2):13~16
55张亦春.现代金融市场学.中国金融出版社, 2002
2郭多祚.数理金融.清华大学出版社, 2006
3 Javiera Aguilar, Stefan Nydahl. Central Bank Intervention and Exchange Rates: the Case of Sweden. Journal of International Financial Markets, Institutions and Money. 2000, (10) :303~322
4田波平.基于广义事件窗的上市公司风险与绩效评价.哈尔滨工业大学管理学院技术经济及管理专业博士论文. 2006:28~32
5孟庆顺.金融资产定价理论的历史回顾与展望.时代经贸. 2006, 4(8):70~72
6伍海华.随机过程―金融资产定价之应用.中国金融出版社, 2002
7 T. Bollerslev. Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics. 1986, 31:307~327
8 Duffie著,潘存武译.动态资产定价理论.上海财经大学出版社, 2003
9冯建芬,陈典发.风险资产的一般定价模型.南开大学学报. 2006
10王振龙.时间序列分析.中国统计出版社, 2003
11王军波,邓述慧.利率、成交量对股价波动的影响――GARCH修正模型的应用.系统工程理论与实践. 1999, (9):49~57
12刘继春.多维GARCH模型的估计和检验.吉林大学自然科学学报. 2000, (4): 56~40
13殷玲,唐杰. GARCH-M模型与我国沪深股市的波动.江南大学学报. 2002, (4): 56~58
14李亚静,朱宏泉,彭育威.基于GARCH模型族的中国股市波动性预测.数学的实践与认识. 2003, (11): 65~71
15惠晓峰,柳鸿生,胡伟等.基于时间序列GARCH模型的人民币汇率预测.金融研究. 2003, (5):3~8
16卢志红,郑丕谔. GARCH族模型在上海股市分阶段对比分析中的应用.中国计量学院学报. 2004, 15(1):58~61
17徐绪松,马莉莉,陈彦斌.我国上海股票市场GARCH效应实证研究.武汉大学学报(理学版). 2002, 48(3):293~296
18唐齐鸣,陈健.中国股市的ARCH效应分析.世界经济. 2001, (3): 29~36
19岳朝龙.上海股市收益率GARCH模型族的实证研究.数量经济技术研究.2001, (6):126~130
20徐剑刚,唐国兴.我国股票市场报酬与波动的GARCH-M模型.数量经济技术经济研究. 1997, 12(12):28~32
21 R. F. Engle, D. M. Lilien and R. P. Robins. Estimating Time-varying Risk Premia in the Term Structure: The ARCH-M Model. Econometrics. 1987, 55: 391~407
22 C. Cao and R. S. Tsay. Nonlinear Time Series Analysis of Stock Volatilities. Journal of Applied Econometrics. 1992, (7):165~185
23 Y. K. Tsey. A Test for Constant Correlations in A Multivariate GARCH Model. Journal of Econometrics. 2000, 98:107~127
24 Istvan. Berkes, Lajos. Horvath, Piotr. Kokoszka. Testing for Parameter Constancy in GARCH (p,q) Models. Statistics & Probability Letters. 2004, 70:263~273
25 Chris. Brooks, P. Simon. Burke, Gita. Persand. Benchmarks and Accuracy of GARCH Model Estimation. International Journal of Forecasting. 2001, 17:45~46
26 R. S. Tsay, D. Pena and A. Pankratz. Outliers in Multivariate Time Series. Biometrika. 2000, 87:789~804
27 R. S. Tsay. Nonlinearity Tests for Time Series. Biometrika. 1996, 73:461~466
28 R. S. Tsay. Analysis of Financial Time Series. Wiley. 2000
29 E. P. Box. Time Series Analysis: Forecasting and Control. Prentice Hall/Pearson. 2005
30 R. S. Tsay. Analysis of Financial Times Series. John Wiley &Sons. Inc. 2002: 20~80
31王耀东,张德远,张海雄.经济时间序列分析.上海财经大学出版社, 1996
32 R. S. Tsay著,潘家柱译.金融时间序列分析.机械工业出版社, 2006
33陈浪南,屈文洲.资本资产定价模型.经济研究. 2000, (4):26~34
34 A. K. Bera and M. L. Higgins. A Test for Conditional Heteroskedasticity in Time Series Models. Journal of Time Series Analysis. 1992, 13:501~519
35 M. L. Higgins, A. K. Bera. A Class of Nonlinear ARCH Models. International Economic Review. 1992, 33:137~158
36 C. J. Ma, M. M. Chen. ARCH Model and Its Application in Finance System. Journal of Hunan University. 1998, 25(5):108~112
37 T. Bollerslev, R. T. Chou, K. F. Kroner. ARCH Modeling in Finance. Journal of Econometrics. 1992, 52:1~59
38 K. P. Mike. So, L. H. Philip. Yub. Empirical Analysis of GARCH Models inValue at Risk Estimation. Int. Fin. Markets Inst. and Money. 2006, (16):180~197
39 Eric. Hillebrand. Neglecting Parameter Changes in GARCH Models. Journal of Econometrics. 2005, 129:121~138
40安鸿志等.时间序列的分析与应用.科学出版社, 1986
41 Teruo. Nakatsuma. Bayesian of Analysis of ARMA-GARCH Models: A Markov Chain Sampling Approach. Journal of Econometrics. 2000, 95:57~69
42 Michael. Sabatini, Oliver. Linton. A GARCH Model of the Implied Volatility of the Swiss Market Index from Option Prices. International Journal of Forecasting. 1998, 14:199~213
43施东晖.上海股票市场风险性研究.经济研究. 1996, (10):35~41
44朱鸣雄.动态资产定价理论中风险估计及其统计性质分析统计研究. 1999, (4):43~46
45李子奈,叶阿忠.高等计量经济学.清华大学出版社, 2000
46陈彦斌.基于财富偏好和习惯形成的资本资产定价模型.武汉大学管理学博士论文. 2003:46~52
47孟庆顺.资产定价模型及其在中国股票市场的检验.吉林大学管理学博士学位论文. 2005:85~94
48孙刚.资本资产定价模型在中国股票市场的实证研究.统计与信息论坛. 2003, (5):91~94
49李和金,李湛.上海股票市场资本资产定价模型实证检验.理论与方法研究. 2000, (5):75~77
50许涤龙,张钰.资本资产定价模型与上海股票市场的实证分析.大学南昌学报(理学版). 2005, (2):142~145
51易丹辉.数据分析与Eviews应用.中国统计出版社, 2002
52周少甫,陈千里.中国股市收益波动的实证研究.华中科技大学学报[J]. 2002, 30(9):48~50
53魏娜. ARCH族模型及其对深圳股票市场的实证分析.数学理论与应用. 2006, (3):60~63
54马静如.资本资产定价模型与深圳股票市场的实证研究.南开经济研究. 2001, (2):13~16
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