我国封闭式基金波动的实证研究
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摘要
从我国基金业的发展历史来看，封闭式基金较早被引入国内。历经数年的发展，封闭式基金运作已经日渐走向成熟，以其在封闭期内基金份额固定的制度特点与开放式基金形成鲜明的对比，构成证券市场当中一只不容忽视的力量。
回顾近年来国内已有的基金文献，绝大多数针对封闭式基金的科研成果集中在基金业绩评价和封闭式基金折价现象的分析与解释。对整个封闭式基金市场以及单只封闭式基金的价格行为与收益波动的研究却十分鲜见，为相关研究留下了较大的创新余地。对基金市场波动的探讨还受到对股票市场波动研究的启发，许多波动研究模型的有效性已经在股市当中得到反复验证，为本文针对基金市场展开的相关研究提供了强有力的理论和方法支持。借鉴股票市场波动研究的方法和经验，本文对我国封闭式基金进行了综合考察，希望从整体上把握封闭式基金市场的运行及其风险特征。
本研究对于投资者、监管者以及基金管理者更好地认识和了解我国封闭式基金近年来的运行情况和风险特征具有重要的意义。首先，广大投资者，特别是保险公司、社保基金等机构投资者可以利用本文的分析结果，按照自己的收益要求和风险偏好构建更加合理的投资组合。其次，对于基会市场的监管者而言，能够在宏观把握近年来我国封闭式基金运行和发展的基础上，有针对性地制定相关规定和政策。从而防范金融市场风险，使我国封闭式基金能够在更加规范的轨道上持续、健康、快速发展。再次，对于基金公司而言，本文的研究将有助于其更好地开展基于风险调整的业绩评价。据此，基金管理者可以针对基金波动进行归因分析，从而采取适当的风险控制措施，不断加强其内部控制提高风险管理水平。
本文试图以实证研究回答以下几个问题：第一，我国封闭式基金市场整体以及封闭式基金个体的收益波动特征究竟是怎样的?第二，我国上海和深圳两个封闭式基金市场指数在不同的市场状况下，如熊市和牛市当中是否存在长期均衡关系?第三，封闭式基金市场的收益波动之间是否存在相互影响?第四，股票市场与基金市场运行之间存在怎样的关系，指数水平波动与指数收益波动之间的相互关系如何?第五，人民币汇率制度改革这样的重大政策事件对我国封闭式基金市场的影响是怎样的?带着上述问题，本文结合“广义的波动”，即指数水平波动与“狭义的波动”，即收益波动，对我国封闭式基金整体与个体进行了系统的研究，得到如下结论：
第一，在对波动模型及其估计方法进行梳理和总结的基础上，本文选用合适的模型分析了我国封闭式基金指数和单只封闭式基金的收益波动特征。实证分析表明，我国封闭式基金整体和个体的收益波动具有聚集性、收敛性，但并不存在显著的非对称性。利用方差方程中引入虚拟变量的波动模型发现，我国封闭式基金指数的收益波动具有显著的周二效应，存在与股票市场类似的市场异象，表明我国封闭式基金市场与股市一样都是缺乏效率的。
第二，在不同市场条件下分别进行的协整分析表明，上海和深圳两个封闭式基金指数之间的长期均衡关系的存在依赖于考察的时间段：熊市当中两市封闭式基金整体表现存在差异，不存在协整关系；但在牛市当中，两个封闭式基金指数之间存在协整关系，表现出同涨同跌的特征。利用多元波动模型对基金指数的考察发现，熊市期间我国两市封闭式基金整体之间存在收益波动的相互影响，而在进入牛市之后，这种相互影响不再显著。
第三，基金指数与股市指数间的长期均衡关系仅在牛市中存在，说明牛市行情中我国封闭式整体上与股票市场是同涨同跌的。在完整的涨跌周期内，股市指数水平波动和收益波动对封闭式基金指数水平波动和收益波动都具有更为显著的引导作用。表明股市对封闭式基金的影响要强于封闭式基金对股市的影响。
最后，本文利用非参数方法考察了2005年我国人民币汇率制度改革带来的人民币窄幅升值对封闭式基金市场的影响。发现此次汇改之后，我国封闭式基金市场指数水平显著上涨，成交量明显放大，而收益波动有所降低。说明人民币升值以及对未来本币进一步升值的预期的确促进了我国封闭式基金市场的繁荣。
全文最后，总结实证研究结果，就目前我国封闭式基金的运行特点与风险特征，向投资者和监管者提出了相关建议，并针对本文的不足对未来的研究提出展望。
According to the development history of China's investment funds, the closed-end funds were introduced into our country earlier. The closed-end funds, which distinguish themselves from open-end funds with the fixed amount of fund shares in their close period, had become more mature and been regarded as a very important investment force after years of development.
The review of existed literature on China's closed-end funds shows that most of the former researchers paid their attention to the assessment of closed-end funds' performance, or the analysis and explanation of closed-end fund puzzle. But little research on the price behavior and volatility of closed-end funds could be found. Such a situation leaves an opportunity for innovations on this topic. Meanwhile, the research on fluctuation and volatility of closed-end funds was also enlightened by similar research in stock market, various kinds of models had been tested in stock markets and been proved effective. These models offered a suit of powerful theory and methodology to this dissertation. In order to grasp the operation and risk features of China's closed-end funds, this dissertation had done a systematic research with the methods and experiences gained from stock market.
From the perspectives of investors, regulators and the managerial companies of funds, this research has a great theoretical and practical meaning of helping them to get a whole picture of the operation and risk features of China's closed-end funds. First of all, the investors, especially the institutional investors such as insurance companies and social security funds could benefit from this research to construct more suitable portfolios following their preference. Second, the regulators of funds could make more suitable and feasible rules and policies to regulate the fund market and control the financial risk with a more profound understanding of the operation and development of closed-end funds. Third, this research could help the managerial companies of funds to improve their risk-adjusted performance assessment process for closed-end funds. With a further attribution analysis of the volatility, fund managers could draw proper counter measures to strengthen their internal control and risk management.
In this dissertation, several questions will be answered empirically. First, what are the volatility features of China's closed-end fund markets and specific closed-end fund? Second, is there a long term equilibrium relationship between the two fund indices in different market situations, such as bear market or bull market? Third, is there an interaction of volatility between the two closed-end fund markets in China? Forth, what is the relationship of stock market indices and closed-end fund indices, and the interaction of their volatilities? Fifth, what is the impact of important political event, such as the RMB exchange rate regime reform in 2005, on the closed-end fund markets. With the questions mentioned above, this dissertation has done a systematic research on both fluctuation and volatility of closed-fund indices and specific funds. The main conclusions of this research are as follows.
First, based on the collection and comparison of volatility models, this dissertation adopted proper models for analyzing the volatility features of fund indices and specific closed-end funds. There are significant clustering and convergence effect, whereas there is no significant asymmetric effect of volatility. By introducing dummy variables into the conditional variance equation of volatility models, this dissertation manifested the significant Tuesday effect of fund indices' volatility. This anomaly, which is similar with that in stock market manifested that our closed-end fund markets are also inefficient.
Second, co-integration analysis under different market situation showed that the long term equilibrium relationship between the two closed-end fund indices is dependent on the research window. The co-integration relation between the two fund indices only existed in the bull market and vanished in the bear market. The two fund markets fluctuate together in the bull market. Further analysis with multivariate GARCH model showed that the volatility interaction between the two closed-end fund markets is significant in bear market and become not significant in bull market.
Third, the long term equilibrium relationship between fund indices and stock market indices only exist in bull market. This manifested that closed-end fund markets fluctuate with stock markets in bull market. From the perspectives of both fluctuation and volatility, the stock markets have a more significant leading impact on closed-end fund markets. The impact from stock markets to closed-end fund markets is much stronger than that from closed-end fund markets to stock markets.
Finally, with the help of nonparametric methods, the impact of the RMB exchange rate regime reform in 2005 on China's closed-end fund market was examined. The empirical results showed that the fund index had been significantly raised after the event with a comparative larger volume and lower volatility. This research firstly offered reliable evidence to the point that the appreciation and the expectation of further appreciation of RMB had promoted the prosperity of our closed-end fund market indeed.
At the end of this dissertation, relevant suggestions are given to investors and regulators in accordance with the empirical research results. Moreover, the prospects for further research are also given based on shortcomings of this research.
回顾近年来国内已有的基金文献，绝大多数针对封闭式基金的科研成果集中在基金业绩评价和封闭式基金折价现象的分析与解释。对整个封闭式基金市场以及单只封闭式基金的价格行为与收益波动的研究却十分鲜见，为相关研究留下了较大的创新余地。对基金市场波动的探讨还受到对股票市场波动研究的启发，许多波动研究模型的有效性已经在股市当中得到反复验证，为本文针对基金市场展开的相关研究提供了强有力的理论和方法支持。借鉴股票市场波动研究的方法和经验，本文对我国封闭式基金进行了综合考察，希望从整体上把握封闭式基金市场的运行及其风险特征。
本研究对于投资者、监管者以及基金管理者更好地认识和了解我国封闭式基金近年来的运行情况和风险特征具有重要的意义。首先，广大投资者，特别是保险公司、社保基金等机构投资者可以利用本文的分析结果，按照自己的收益要求和风险偏好构建更加合理的投资组合。其次，对于基会市场的监管者而言，能够在宏观把握近年来我国封闭式基金运行和发展的基础上，有针对性地制定相关规定和政策。从而防范金融市场风险，使我国封闭式基金能够在更加规范的轨道上持续、健康、快速发展。再次，对于基金公司而言，本文的研究将有助于其更好地开展基于风险调整的业绩评价。据此，基金管理者可以针对基金波动进行归因分析，从而采取适当的风险控制措施，不断加强其内部控制提高风险管理水平。
本文试图以实证研究回答以下几个问题：第一，我国封闭式基金市场整体以及封闭式基金个体的收益波动特征究竟是怎样的?第二，我国上海和深圳两个封闭式基金市场指数在不同的市场状况下，如熊市和牛市当中是否存在长期均衡关系?第三，封闭式基金市场的收益波动之间是否存在相互影响?第四，股票市场与基金市场运行之间存在怎样的关系，指数水平波动与指数收益波动之间的相互关系如何?第五，人民币汇率制度改革这样的重大政策事件对我国封闭式基金市场的影响是怎样的?带着上述问题，本文结合“广义的波动”，即指数水平波动与“狭义的波动”，即收益波动，对我国封闭式基金整体与个体进行了系统的研究，得到如下结论：
第一，在对波动模型及其估计方法进行梳理和总结的基础上，本文选用合适的模型分析了我国封闭式基金指数和单只封闭式基金的收益波动特征。实证分析表明，我国封闭式基金整体和个体的收益波动具有聚集性、收敛性，但并不存在显著的非对称性。利用方差方程中引入虚拟变量的波动模型发现，我国封闭式基金指数的收益波动具有显著的周二效应，存在与股票市场类似的市场异象，表明我国封闭式基金市场与股市一样都是缺乏效率的。
第二，在不同市场条件下分别进行的协整分析表明，上海和深圳两个封闭式基金指数之间的长期均衡关系的存在依赖于考察的时间段：熊市当中两市封闭式基金整体表现存在差异，不存在协整关系；但在牛市当中，两个封闭式基金指数之间存在协整关系，表现出同涨同跌的特征。利用多元波动模型对基金指数的考察发现，熊市期间我国两市封闭式基金整体之间存在收益波动的相互影响，而在进入牛市之后，这种相互影响不再显著。
第三，基金指数与股市指数间的长期均衡关系仅在牛市中存在，说明牛市行情中我国封闭式整体上与股票市场是同涨同跌的。在完整的涨跌周期内，股市指数水平波动和收益波动对封闭式基金指数水平波动和收益波动都具有更为显著的引导作用。表明股市对封闭式基金的影响要强于封闭式基金对股市的影响。
最后，本文利用非参数方法考察了2005年我国人民币汇率制度改革带来的人民币窄幅升值对封闭式基金市场的影响。发现此次汇改之后，我国封闭式基金市场指数水平显著上涨，成交量明显放大，而收益波动有所降低。说明人民币升值以及对未来本币进一步升值的预期的确促进了我国封闭式基金市场的繁荣。
全文最后，总结实证研究结果，就目前我国封闭式基金的运行特点与风险特征，向投资者和监管者提出了相关建议，并针对本文的不足对未来的研究提出展望。
According to the development history of China's investment funds, the closed-end funds were introduced into our country earlier. The closed-end funds, which distinguish themselves from open-end funds with the fixed amount of fund shares in their close period, had become more mature and been regarded as a very important investment force after years of development.
The review of existed literature on China's closed-end funds shows that most of the former researchers paid their attention to the assessment of closed-end funds' performance, or the analysis and explanation of closed-end fund puzzle. But little research on the price behavior and volatility of closed-end funds could be found. Such a situation leaves an opportunity for innovations on this topic. Meanwhile, the research on fluctuation and volatility of closed-end funds was also enlightened by similar research in stock market, various kinds of models had been tested in stock markets and been proved effective. These models offered a suit of powerful theory and methodology to this dissertation. In order to grasp the operation and risk features of China's closed-end funds, this dissertation had done a systematic research with the methods and experiences gained from stock market.
From the perspectives of investors, regulators and the managerial companies of funds, this research has a great theoretical and practical meaning of helping them to get a whole picture of the operation and risk features of China's closed-end funds. First of all, the investors, especially the institutional investors such as insurance companies and social security funds could benefit from this research to construct more suitable portfolios following their preference. Second, the regulators of funds could make more suitable and feasible rules and policies to regulate the fund market and control the financial risk with a more profound understanding of the operation and development of closed-end funds. Third, this research could help the managerial companies of funds to improve their risk-adjusted performance assessment process for closed-end funds. With a further attribution analysis of the volatility, fund managers could draw proper counter measures to strengthen their internal control and risk management.
In this dissertation, several questions will be answered empirically. First, what are the volatility features of China's closed-end fund markets and specific closed-end fund? Second, is there a long term equilibrium relationship between the two fund indices in different market situations, such as bear market or bull market? Third, is there an interaction of volatility between the two closed-end fund markets in China? Forth, what is the relationship of stock market indices and closed-end fund indices, and the interaction of their volatilities? Fifth, what is the impact of important political event, such as the RMB exchange rate regime reform in 2005, on the closed-end fund markets. With the questions mentioned above, this dissertation has done a systematic research on both fluctuation and volatility of closed-fund indices and specific funds. The main conclusions of this research are as follows.
First, based on the collection and comparison of volatility models, this dissertation adopted proper models for analyzing the volatility features of fund indices and specific closed-end funds. There are significant clustering and convergence effect, whereas there is no significant asymmetric effect of volatility. By introducing dummy variables into the conditional variance equation of volatility models, this dissertation manifested the significant Tuesday effect of fund indices' volatility. This anomaly, which is similar with that in stock market manifested that our closed-end fund markets are also inefficient.
Second, co-integration analysis under different market situation showed that the long term equilibrium relationship between the two closed-end fund indices is dependent on the research window. The co-integration relation between the two fund indices only existed in the bull market and vanished in the bear market. The two fund markets fluctuate together in the bull market. Further analysis with multivariate GARCH model showed that the volatility interaction between the two closed-end fund markets is significant in bear market and become not significant in bull market.
Third, the long term equilibrium relationship between fund indices and stock market indices only exist in bull market. This manifested that closed-end fund markets fluctuate with stock markets in bull market. From the perspectives of both fluctuation and volatility, the stock markets have a more significant leading impact on closed-end fund markets. The impact from stock markets to closed-end fund markets is much stronger than that from closed-end fund markets to stock markets.
Finally, with the help of nonparametric methods, the impact of the RMB exchange rate regime reform in 2005 on China's closed-end fund market was examined. The empirical results showed that the fund index had been significantly raised after the event with a comparative larger volume and lower volatility. This research firstly offered reliable evidence to the point that the appreciation and the expectation of further appreciation of RMB had promoted the prosperity of our closed-end fund market indeed.
At the end of this dissertation, relevant suggestions are given to investors and regulators in accordance with the empirical research results. Moreover, the prospects for further research are also given based on shortcomings of this research.
引文
1 本文与2005年11月完成开题，在2006年初以后，国内关于基金指数水平波动和收益波动的研究的文献开始有少量的涌现。
2 在英语中，共同基金mutual fund的mutual意为joint(联合)，而fund有holding(控制)之意，即把许多人的钱集中起来进行专业化投资的运作。共同基金其实就是一类投资公司investment company。作为公司，每一个共同基金都有各自不同的经理、员工、运作方式和目标等等。基金的投资目标反映其成立和存在的理由。简而言之，共同基金集合了一部分委托人的资金，并代表他们的利益进行有预设目的投资。每个基金公司都会雇佣投资专业人士来管理基金的投资组合，通常称他们为投资组合管理者。这些专业人士可以组成团队来经营基金，有些投资公司甚至委托其他公司或自由投资专业人士来帮助公司进行资本运作。
3 从2006年11月开始，已经有封闭式基金陆续到期，有的已经进入封转开进程，而有的可能面临清盘的命运。从而使得我国封闭式基金总体规模呈现出逐渐缩小的趋势。
4 由于开放式基金较晚引入我国，所以2002年以前国内发表的证券投资基金研究成果基本是针对封闭式基金进行的。此后，国内依然有大量关于封闭式基金的研究涌现出来。
5 参见Poon和Granger(2003)对国外主要波动研究模型的分类。
6 参见Engle R F, Risk and Volatility: Econometric Models and Financial Practice[J]. American Economic Review, 2004, 94(3): 405-420.
7 对不同市场间收益波动的相互影响更为流行的称谓应当是“联动”(Co-movement)。但鉴于国内有的实证文章中把指数间的关联性也称为联动，为避免混淆，本文将指数间收益波动的相互影响统称为“波动关系”。
1 该5条标准由计量经济学家A．C．Harvey提出，参见Harvey,A．C．Economic Analysis of Time Series[M]，Wiley，New York，1981，p5-7．
2 Friedman N, Geiger D, Goldszmidt M. Bayesian network classifiers [J]. Machine Learning,1997, 29: 131～161.
3 根据统计学知识，正态分布的偏度为0，峰度为3。若序列的统计特征显著偏离上述数值，则认为数据不服从正态分布。
4 参见王春峰．VaR金融市场风险管理[M]．天津：天津大学出版社，2001．
5 参见高铁梅．计量经济分析方法与建模：EVIEWS应用及实例[M]．清华大学出版社2006，P175-176．
6 参见高铁梅．计量经济分析方法与建模：EVIEWS应用及实例[M]．清华大学出版社2006，P128．
7 关于长记忆波动模型的介绍可参见张世英，樊智．协整理论与波动模型——金融时间序列分析及应用[M]．北京：清华大学出版社，2004．
8 由于对角多元GARCH简化了多个变量之间的相关关系，因而该模型无法用于研究多个市场间的波动相互关系和溢出效应。
9 Berndt, E. R, Hall, B. H., Hall, R. E and Hausman. Estimation and inference in nonlinear structural models. Annual of Economic and Social Measurement, 1974(4): 653-665.
1 参见蔡(Tsay)著《金融时间序列分析》中译本，机械工业出版社，2006版第73页。
2 以该办法的出台为分水岭，此前的基金成为老基金，此后上市的基金品种成为新基金。
3 称其为新基金是相对于98年前成立的老基金而言。
4 截至2006年12月我国封闭式基金尚有53只。基金兴业(sh5000283已经与2006年底成功转为开放式基金——华夏稳健增长，成为国内首家基金封转开的成功案例。
5 从模型估计结果看，针对单只封闭式基金的GARCH-GED模型建模效果均不够理想，所以此处采用t分布与GARCH模型组合进行估计。下文中针对单只封闭式基金的EGARCH模型建模采用t分布假设基于相同的考虑。
6 与GARCH模型的情况类似，从模型估计结果看，针对单只封闭式基金的EGARCH-GED模型建模效果均不够理想，所以此处采用t分布与EGARCH模型组合进行估计。
7 其他主要的日历异常有一月效应，月度更替效应和假日效应。
8 大量研究表明，证券收益率的分布普遍具有尖峰厚尾现象。本文第3章已经阐明我国基金市场的收益同样存在尖峰厚尾特征。
1 如本文第三章所述，封闭式基金指数的收益序列均没有显著的自相关，收益围绕一个均值水平波动，可以采用简单的均值方程设定。当然，研究中可以假定条件均值服从ARMA过程，然而这种假定会使得模型的估计更为复杂。根据Nelson(1990)，均值方程中滞后阶数的选择对条件方差方程的参数估计不产生实质影响，因此这里同样选取简化方程形式。
2 关于GARCH族类模型滞后阶数的选取尚缺乏为大家共同接受的标准，以往的研究一般认为GARCH项和ARCH项滞后阶数都取为1便足以描述金融市场的波动状况。例如，Lamoureux等(1993)认为GARCH(1，1)或者EGARCH(1，1)形式能够很好地评估条件方差，其他支持GARCH(1，1)形式的证据可见汉密尔顿(1994)。如下文所述，向量GARCH(1，1)形式对于我国市场也是合适的。
3 参见黄大海2004。另外，由于提高多元GARCH模型的滞后阶数会极大的增加模型中参数个数，增加估计的困难程度。并且在一元GARCH建模中，一般选取形式最为简单的GARCH(1，1)便可以取得较好的建模效果。所以，这里选择BEKK(1，1)模型来进行研究。
1 截至2006年6月，我国封闭式基金的发行份额为817亿，远远小于股市规模。
2 此处均值方程中包含的是条件标准差的一阶之后，与上证综合指数的均值方程设定不同。
3 根据定义，协整检验(Co-integration Test)只能在同阶的非平稳序列间进行。
[1] Andersen T G, Bollerslev T. Answering the critics: yes, ARCH models do provide good volatility forecasts[J]. International Economic Review, 1998, 39(4): 885-905.
[2] Baillie R T. Long memory processes and fractional integration in econometrics [J]. Journal of Econometrics, 1996, 73(1): 5-59.
[3] Baillie, R. T, Bollerslev, T. Mikkelsen, H. O. Fractionally integrated generalized autoregressive conditional heteroskedasticity[J]. Journal of Econometrics, 1996, (74): 3-30.
[4] Banerjee, A., Dolado, J. J., Hendry, D. F., Smith, G. W. Exploring equilibrium relationships in econometrics through static models: Some monte carlo Evidence[J]. Oxford Bulletin of Economics and Statistics, 1986, (48): 253-277.
[5] Banerjee, A., Hendry, D. F. Testing Integration and cointegration: An overview[J]. Oxford Bulletin of Economics and Statistics, 1992, (54): 225-255.
[6] Bekart, Harvey, Bekart, G., Harvey, C. R. Emerging equity market volatility[J]. Journal of Financial Economics, 1997, (43): 29-77.
[7] Bera, A. K., M. L. Higgins. A test for conditional heteroskedasticity in time series models[J]. Journal of Time Series Analysis, 1992, (13): 501-19.
[8] Bera A. K., Higgins M. L. ARCH Models: Properties, Estimation and Testing[J]. Journal of Economic Surveys, 1993, (7): 305-366.
[9] Berndt E K, et al. Estimation inference in nonlinear structural models[J]. Annals of Economic and Social Measurement, 1974, (4): 653-665.
[10] Berndt, E. R, Hall, B. H., Hall, R. E and Hausman. Estimation and inference in nonlinear structural models[J]. Annual of Economic and Social Measurement, 1974, (4): 653-665.
[11] Bollerslev T, et al. A capital-asset pricing model with time-varying coefficients [J]. Journal of Political Economy, 1988, (96): 116-131.
[12] Bollerslev T, Mikkelsen H. O. Modeling and pricing long memory in stock market volatility[J]. Journal of Econometrics, 1996, (73): 151-184.
[13] Bollerslev, T., Engle R. F., Wooldridge, J. M., A capital asset pricing model with time-varying covariance[J], Journal of Political Economy, 1988, (96): 116-131.
[14] Bollerslev T. Modeling the coherence in short_run nominal exchange rates: A multivariate generalized ARCH model[J]. The Review of Economics and Statictics, 1990, (72): 498-505.
[15] Bollerslev, T., R. Y. Chou, K. F. Kroner. ARCH Modeling in Finance: A Review of the Theory and Empirical Evidence[J]. Journal of Econometrics, 1992, (52): 5-59.
[16] Bollerslev, Tim. Generalized Autoregressive Conditional Heteroskedasticity [J]. Journal of Econometrics, 1986, (31): 307-327.
[17] Boswijk, H. P.. Estimation and testing for cointegration with trended variables: A comparison of a static and a dynamic regression procedure[R]. Netherland: University of Amsterdam, 1989.
[18] Bollerslev, T., R. Y. Chou, and K. F. Kroner. ARCH Modeling in Finance: A Review of the Theory and Empirical Evidence[J]. Journal of Econometrics, 1992, (52): 5-59.
[19] Bollerslev, T., Robert F. Engle and Daniel B. Nelson. 1994. "ARCH models" in Handbook of Econometrics[M]. Vol. Ⅳ. Robert F. Engle and D. L. McFadden, eds. Amsterdam: North-Holland, 2959-3038.
[20] Boswijk, H. P. Testing for cointegration in structural models[R]. Netherland: University of Amsterdam, 1991.
[21] Brailsford, Timothy J. and Robert TV, Faff. An evaluation of volatility forecasting techniques[J]. Journal of Banking and Finance, 1996, (20): 419-438.
[22] Breidt, F. J.. A Threshold Autoregressive Stochastic Volatility Model[J]. Iowa State University Working paper, 1996.
[23] Brooks C,. A double Threshold GARCH Model for the French Frank/German Mark Exchange Rate Journal of Forecasting, 2001, (20): 135-143.
[24] Cao, Charles Q. and Ruey S. Tsay. Nonlinear Time-Series Analysis of Stock volatilities[J]. Journal of Applied Econometrics, 1992, (1): 165-85.
[25] Chiang, T. C., Doong, S.. Empirical analysis of stock returns and volatility: Evidence from Seven Asian Stock markets based on TAR-GARCH model[J]. Review of Quantitative Finance and Accounting, 2001, (17): 301-318.
[26] Clare Andrew, Ian Garrett, Greg Jones. Testing for seasonal patterns in conditional return volatility: evidence fromAsia-Pacific markets[J]. Applied Financial Economics, 1997, (7): 517-523.
[27] Clark P K. A subordinated stochastic process model with finite variance for speculative prices[J]. Econometrica, 1973, (41): 135-155.
[28] Cross F. Price movements on fridays and mondays[J]. Financial Analysts Journal, 1973, 29(6): 67-69.
[29] DeBondt, W. F. M. and R. H. Thaler. Does the stock marketoverreact? [J]. Journal of Finance, 1985, (40): 793-808.
[30] Dickey, D. A. and W. A. Fuller. Distribution of the Estimators for Autoregressive Time Series with a Unit Root[J]. Journal of the American Statistical Association, 1979, (74): 427-431.
[31] Diebold, Francis X. and Jose A. Lopez. 1995. "Modelling volatility Dynamics" in Macroeconomics: Developments[M], Tensions and Prospects. Kevin D. Hoover, ed. Boston, Dordrecht, and London: Kluwer Academic, 427-466.
[32] Ding Zhuangxin. C. W. J, Granger, and R. F. Engle. ALong Memory Property of Stock Market Returns and A New Model[J]. Journal of Empirical Finance, 1993, (1): 83-106.
[33] Dirk Baur and Robert Jung. Return and volatility linkages between the US and the German stock market[J]. Journal of International Money and Finance 2006(25): 598-613.
[34] Engle, R. F, Kroner K F. Multivariate simultaneous generalized ARCH [J]. Econometric Theory, 1995, (11): 122-150.
[35] Engle, R. F. Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of U. K. Inflation[J]. Econometrica, 1982, (50): 987-1008.
[36] Engle, R. F. and V. K. Ng. Measuring and Testing the Impact of News on Volatility[J]. Journal of Finance, 1993, (48): 1749-1778.
[37] Engle, R. F. & C. W. J. Granger. Co-integration and error correction: representation, estimation andtesting[J]. Econometrica, 1987, (55): 251-276.
[38] Engle, R. F. and Kraft, D. F. Multi period Forecast Error Variances of Inflation Estimated from ARCH Models[M]. Applied Time Series Analysis of Economic Data, 1983: 293—302.
[39] Engle R. F., Lilien D. M., and Robins R. P.. Estimating time varying risk premia in the term structure: the ARCH-M model[J]. Econometrica, 1987, 55: 391-407.
[40] Engle, R. F.. Risk and Volatility: Econometric Models and Financial Practice[J]. American Economic Review, 2004, 94 (3): 405-420.
[41] Engle, R. F., Takatoshi Ito, and Wen-Ling Lin. Meteor Showers or HeatWawes? Heteroskedastic Intra-Daily Volatility in the Foreign Exchange Market[J], Econometrica, 1990, (58): 525.
[42] Engle R. F. and Susmel R., 1993, Common volatility in international equality markets, Journal of business and economics statistics, 1993, (11): 167-176.
[43] Fama, Eugene F. Stock Returns, Real Activity, Inflation, and Money[J]. American Economic Review, 1990, (71): 545-565.
[44] Fama, Eugene F. The Behavior of Stock Market Price[J]. Journal of Business, 1965, (38): 34-105.
[45] Fang, W. The effects of currency depreciation on stock returns: Evidence from five East Asian economies[J]. Applied Economics Letters, 2002, (9): 195-199.
[46] Flannery, M., Protopapadakis, A.. Macroeconomic factors do Influence aggregate stock returns[J]. Review of Financial Studies, 2002, (15): 751-782.
[47] Forbes, Kristin J. and Roberto Rigobon. No Contagion, Only Interdependence: Measuring Stock Market Comovements[J]. Journal of Finance, 2002, (57): 2223-2261.
[48] French K R, Schwert G W, Stambaugh R F. Expected stock returns and volatility[J]. Journal of Financial Economics, 1987, (19): 3-29.
[49] Friedman N, Geiger D, Goldszmidt M. Bayesian network classifiers[J]. Machine Learning, 1997, (29): 131-161.
[50] Fuller, W. A. Introduction to statistical time series[M]. New York: John Wiley and Sons, 1976.
[51] Gibbons M, Hess P. Day of the week effects and asset returns [J]. Journal of Business, 1981, 54(5): 579-596.
[52] Glosten, L. R., Jagannathan, D. Runkle. On the Relation Between the Expected Value and the Volatility of the Nominal Excess Return on Stocks[J]. Journal of Finance, 1993, (48): 1779-1801.
[53] Granger, C. W. J. Investigating Causal Relations by Econometric Models and Cross-Spectral Methods[J]. Econometrica, 1969, 37 (3): 424-38.
[54] Granger, C. W. J., Machina, M. J. Structural Attribution of Observed Volatility Clustering[J]. Journal of Econometrics, 2006, (135): 15-29.
[55] Gray S. F. Modeling the Conditional Distribution of Interest Rates as a Regime-switching Process[J]. Journal of Financial Economics, 1996, (42): 27-62.
[56] Ghysels, Eric; Andrew Harvey and Eric Renault. "Stochastic Volatility," in Handbook of Statistics: Statistical Methods in Finance, Vol. 14. G.S. Maddala and C. R. Rao, eds. Amsterdam: Elsevier Science, 1996. 119-191.
[57] Hamao, Y., Masulis, R. W. Ng, V. Correlationsin Price Changes and Volatility across International Stock Markets[J]. Review of Financial Studies, 1990, (3): 281-307.
[58] Harvey A. C., Shephard N. The estimation of an asymmetric stochastic volatility model for asset returns [J]. Journal of Business and Economic Statistics, 1996, (14): 429-434.
[59] Harvey A. C., E. Ruiz and N. Shephard. Multivariate Stochastic Volatility Models[J]. Review of Economic Studies, 1994, (61): 247-264.
[60] Harvey, A. C. Economic Analysis of Time Series[M]. New York: Wiley, 1981: 5-7.
[61] Harvey C R, Whaley R E. Market volatility prediction and the efficiency of the S&P100 index option market. Journal of Financial Economics, 1992, (31): 43-74.
[62] Heynen, Ronald C. and Hany M. Kat. Volatility Prediction: A Comparison of Stochastic Volatility, GARCH(1,1) and EGARCH(1,1)Models[J]. Journal of Deriuatiues 1994, (2): 50-65.
[63] Ho Y. K., Cheung Y. L. Seasonal pattern in volatility in Asian stock markets[J]. Applied Financial Economics, 1994, (4): 61-67.
[64] Hull, John and Alan White. The pricing of options on assets with stochastic volatilities[J]. Journal of Finance, 1987, 42(2): 281-300.
[65] Jacquier, E. Polson, N. G, Rossi, P. E. Bayesian analysis of stochastic volatility models[J]. Journal of Business and Economic Statistics, 1994, (12): 371-389.
[66] Jeffrey Pontiff. Excess Volatility and Closed-End Funds[J]. The American Economic Review, 1997, 87(1): 155-169.
[67] Jeon, B. N., Von Furstenberg, G. M., 1990. Growing international co-movement in stock price indexes[J]. Quarterly Review of Economics and Business(30): 15-30.
[68] Johansen S. J. Testing weak exogeneity and the efficiency of single equation analysis[J]. Journal of Econometrics, 1992, (52): 389-402.
[69] Johansen, Soren. Estimation and Hypothesis Testing of Cointegration Vectors in Gaussioan Vector Autoregressive Models[J]. Econometrica, 1991, (59): 1551-1580.
[70] Johansen, Soren. Likelihood-based Inference in Cointegrated Vector Autoregressive Models[M]. Oxford: Oxford University Press, 1995.
[71] Johnsen, S. J. Statistical analysis of cointegration vectors[J]. Journal of Economic Dynamics and Control, 1988, (12): 231-254.
[72] Johnsen, S. J. and Juselius, K. Maximum likelihood estimation and inference on cointegration—with Application to the demand for money[J]. Oxford Bulletin of Economics and Statistics, 1990, (52): 169-210.
[73] Kearney, C., Andrew Patton, Multivariate GARCH modeling of exchange rate volatility transmission in the European monetary system[J], The Financial Review, 2000, (41): 29-48.
[74] Kim S. Shephard N. Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models[R]. Nuffield College, London: Oxford University, 1994.
[75] King, Mervyn and Sushil Wadhwani., Transmission of Volatility between Stock Markets[J]. Review of Financial Studies, 1990, (3): 5-33.
[76] King, Mervyn, Enrique Sentana and Sushil Wadhwani. Volatillity and Links between National Stock Markets[J]. Econometrica, 1994, (62): 901-933.
[77] Klaassen, F. Improving Garch Volatility Forecasts with Regime-Switching GARCH. Empirical Economics, 2002, (27): 363-394.
[78] Koopman S J, Uspensky E H. The stochastic volatility in mean model: Empirical evidence from international stock markets[J]. Journal of Applied Econometrics, 2002, (17): 667-689.
[79] Lee, Keun Yeong. Are the GARCH Models Best in Out-Of-Sample Performance? [J]. Economic Letters, 1991, 37(3): 305-308.
[80] Li, Q. et al. The relationship between stock returns and volatility in international stock markets[J]. Journal of Empirical Finance, 2005, (12): 650-665.
[81] Lin, W., Engle, R. F. Ito, T. Do Bulls and Bears Move across Borders? International Transmission of Stock Returns and Volatility[J]. Review of Financial Studies, 1994, (7): 507-538.
[82] Mandelbrot. The variation of certain speculative prices[J], Journal of Business, 1963, (36): 394-419.
[83] Merville, L. H., and D. R. Pieptea. Stock Price Volatility, Mean-Reverting Diffusion, and Noise[J]. Journal of Financial Economics, 1989, (24): 193-214.
[84] Michael J. Dueker. Markov Switching in GARCH Processes and Mean-Reverting Stock-Market Volatility[J]. Journal of Business & Economic Statistics, 1997, (15); 26-34.
[85] Nelson, Daniel B. Conditional Heteroskedasticity in Asset Returns: A New Approach[J]. Econometrica, 1991, (59): 347-370.
[86] Ng, V. K., Chang, R. P. and Chou, R. Y. An examination of the behaviour of Pacific-Basin stock market volatility[J], Pacific-Basin Capital Markets Research, 1991, (2): 245-260.
[87] Nicholls, D. F., and B. G. Quinn. Random Cofficient Autoregressive Models: An Introduction[M]. New York: Lecture Notes in Statistics, 1999: 11.
[88] Nikkinen, J. et al. Global stock market reactions to scheduled U. S. macroeconomic news announcements[J]. Global Finance Journal, 2006, (17): 92-104.
[89] Pagan, A. R., and G. W. Schwert. Alternative Models for Conditional Stock Volatility[J]. Journal of Econometrics, 1990, (45): 267-290.
[90] Phillips, P. C. B. Time series regression with a unit root[J]. Econometrica, 1987, (55): 277-301.
[91] Phillips, P. C. B. Towards a unified asymptotic theory for autoregression [J]. Biometrika, 1987, (74): 535-547.
[92] Phillips, P. C. B. Regression theory for near-integrated time series[J]. Econometrica, 1988, (56): 1021-1043.
[93] Phillips, P. C. B. and P. Perron. Testing for a Unit Root in Time Series Regression[J]. Biometrika, 1988, (75): 335-346.
[94] Poon S-H, Granger C. Forecasting volatility in financial markets: a review[J]. Journal of Economic Literature, 2003, (41): 478-539.
[95] Poterba, James M and Lawrence H. Summers. The persistence of volatility and stock market fluctuations[J]. American Economic Review, 1986, (76): 1142-1151.
[96] Robert J. Shiller. Comovements in Stock Prices and Comovements in Dividends[J]. The Journal of Finance, 1989, 44(3): 719-729.
[97] Robert J. Shiller. Market Volatility and Investor Behavior[J]. The American Economic Review, 1990, 80(2): 58-62.
[98] Schwert, W. G. Why does stock market volatility change over time?[J]. Journal of Finance, 1989, (44): 1368-1388.
[99] Sheikh A M. The behavior of volatility expectations and their effects on expected returns. Journal of Business, 1993, (66): 93-116.
[100] Smith, Daniel R. Markov-Switching and Stochastic Volatility Diffusion Models of Short-Term Interest Rates[J]. Journal of Business & Economic Statistics, 2002, 20(2): 183-197.
[101] So M. K., and Lain, K., and Li, W. K. A stochastic volatility model with Markov switching[J]. Journal of Business& Economic Statistics, 1998, (16): 244-253.
[102] Tauchen G., Pitts M. The price variability-volume relationship on speculative markets[J]. Econometrica, 1983, (51): 485-505.
[103] Taylor S. Modeling financial time series[M]. New York: Wiley and Sons, 1986.
[104] Tsay, R. S. Conditional Heteroscedastic Time Series Models[J]. Journal of the American Statistical Association, 1987, (82): 590-604.
[105] Wang, P., A. Liu, Return and risk interaction in Chinese stock markets, Journal of International Financial Markets[J], Institutions & Money, 2004, (14): 367-384.
[106] Zakoian, J. M. Threshold heteroskedasticmodels[J]. Journal of Economic Dynamics and Control, 1994, (18): 931-955.
[107] Zakoian, J. M. Threshold HeteroskedasticityModels[M]. Paris: Manusctipt, CREST, INSEE, 1990.
[108] 蔡(Tsay)著，潘家柱译．金融时间序列分析[M]．机械工业出版社，2006．
[109] 陈娟，沈晓栋．中国股票市场收益率与波动性的阶段性研究[J]．统计与决策，2005，(08)：98-100．
[110] 陈浪南，黄杰鲲．中国股票市场波动非对称性的实证研究[J]．金融研究，2002，(5)：67-73．
[111] 陈梦根．中国证券市场下一步发展的路径选择[J]．财贸经济，2004，(8)：62-67．
[112] 陈千里．股市波动的周内效应研究[J]．湖北大学学报(自然科学版)，2003，(1)：29-32．
[113] 陈学彬，傅东升，葛成杰．我国居民个人生命周期消费投资行为动态优化模拟研究[J]．金融研究，2006，(2)：21-35．
[114] 陈永新．中国股市与宏观经济形势的背离及原因分析[J]．云南财贸学院学报，2005，21(2)：34-38．
[115] 樊智，张世英．多元GARCH建模及其在中国股市分析中的应用[J]．管理科学学报，2003，(4)：68-73．
[116] 奉立城．中国股票市场的“周内效应”[J]．经济研究，2000，(11)：50-56．
[117] 高铁梅．计量经济分析方法与建模：EVIEWS应用及实例[M]．北京：清华大 学出版社，2006．
[118] 郭晓亭，林略．证券投资基金风险的表现形式及特征[J]． 商业研究，2004，(23)：138-140．
[119] 郭晓亭．中国证券投资基金市场波动特征实证研究[J]．中国管理科学，2006，(01)：15-20．
[120] 贺京同，沈洪溥．基于行为金融学的我国基金价格波动研究[J]．经济学动态，2002，(6)：42-46．
[121] 洪永淼、成思危等．中国股市与世界其它股市之间的大风险溢出效应[A]．见：2004年中国虚拟经济年会．天津：2004．
[122] 胡国军．中国证券市场波动率的实证研究[D]．上海：复旦大学硕士学位论文，2005．
[123] 黄炳艺，曾五一．证券投资基金市场与股票市场运行特征实证分析[J]．天津财经学院学报，2004，(5)：23-25．
[124] 黄大海．中国股票市场价格波动的理论与实证研究[D]．天津大学，2004．
[125] 蒋璐．股票价格波动与货币政策的关联性研究——我国1997-2004年的实证分析[D]．上海：复旦大学硕士学位论文，2005．
[126] 金洪飞．上海股市(1997．7-2003．5)的波动性、风险溢价和周末效应分析[J]．清华大学中国经济研究中心工作论文，2005．
[127] 金晓斌，高道德，石建民，刘红忠．中国封闭式基金折价问题实证研究[J]．中国社会科学，2002，(5)：55-66．
[128] 李峰．不同波动特性下金融市场风险计量与规避[D]．长沙：湖南大学硕士学位论文，2003．
[129] 李汉东，张世英．ARCH模型与SV模型之间关系研究[J]．系统工程学报，2003，18(2)：97-103．
[130] 李子奈，叶阿忠．高等计量经济学[M]．北京：清华大学出版社，2000．
[131] 刘金全，崔畅．中国沪深股市收益率和波动性的实证分析[J]．经济学(季刊)，2002，(1)：885-898．
[132] 刘晓星，何建敏，刘庆富．基于VaR-EGARCH-GED模型的深圳股票市场波动性分析．南开管理评论，2005，(8)：9-13．
[133] 陆慰祖．高等时间序列经济计量学[M]．上海：上海人民出版社，1999．
[134] 罗洪浪，王浣尘．封闭式基金的超额波动性研究[J]．证券市场导报，2003，(10)．
[135] 马超群，陈牡妙．金融系统ARCH模型及应用[J]．湖南大学学报，1998，(5)：108-112．
[136] 马超群，傅安里，杨晓光．中国投资基金波动择时能力的实证研究[J]．中国管理科学，2005，(4)：22-28．
[137] 孟祥友．封闭式基金市场波动特征研究[D]．成都：西南财经大学，2006．
[138] 莫扬．股票市场波动性的国际比较研究[J]．数量经济技术经济研究，2004，(10)：49-56．
[139] 牛方磊，卢小广．基于ARCH类模型的基金市场波动性研究[J]．统计与决策，2005，(24)：109-110．
[140] 牛方磊．我国封闭式基金市场关联性与价格波动研究[D]．河海大学，2006．
[141] 祁斌．机构投资者发展与市场有效性及稳定性研究中国证券网[N]．上海证券报，2005，12(23)．
[142] 钱争鸣．ARCH族计量模型再金融市场研究中的应用[J]． 厦门大学学报(哲学社会科学版)，2000，(3)：126-129．
[143] 沈洪溥，周立群．行为金融学对封闭式基金价格波动的解释[J]．金融教学与研究，2003，(5)：41-43．
[144] 宋逢明，李翰阳．中国股票波动性的分解实证研究[J]．财经论丛，2004，(7)：23-28．
[145] 唐齐鸣，陈健．中国股市的ARCH效应分析[J]．世界经济，2001，(3)：29-36．
[146] 汪素南，潘云鹤．美国股市与中国股市间溢出效应的实证研究[J]．浙江大学学报(工学版)，2004，38(11)：1431-1435．
[147] 王春峰．VaR金融市场风险管理[M]．天津：天津大学出版社，2001．
[148] 王明涛等．证券市场风险测度——管理模型研究[M]．上海：上海财经大学出版社，2006．
[149] 王明照，郭冰．金融资产波动性特征研究回顾[J]．经济学动态，2005，(1)：76-81．
[150] 王新宇，宋学锋，吴瑞明．中国证券市场市场的分形分析[J]．管理科学学报，2004，7(5)：67-74．
[151] 温素彬．我国股市波动的ARCH类模型分析[J]．淮海工学院学报，2002，(2)：64-67．
[152] 许承明，宋海林．中国封闭式基金价格报酬过度波动的经验分析[J]．经济研究，2005，(3)：108-118．
[153] 杨湘豫，周屏．GARCH模型在开放式基金中的实证研究[J]．系统工程，2006，(04)：73-76．
[154] 殷玲，唐杰．GARCH-M模型与我国沪深股市的波动[J]．江南大学学报，2002，(4)：56-58．
[155] 詹姆斯D．汉密尔顿．时间序列分析[M]．北京：中国社会科学出版社，1999．
[156] 张俊生．中国封闭式证券投资基金折价交易的实证分析[J]．经济科学，2001，(2)：50-55．
[157] 张世英，柯珂．ARCH模型体系[J]．系统工程学报，2002，(3)：236-245．
[158] 张世英，樊智．协整理论与波动模型——金融时间序列分析及应用[M]：北京：清华大学出版社，2004．
[159] 张晓峒．计量经济分析[M]．北京：经济科学出版社，2001．
[160] 张宗新．经济转型期证券资产价格波动的制度解析[J]．数量经济技术经济研究，2005，(1)：18-33．
[161] 周俊，中国股票市场价格波动数量分析[D]．成都：西南财经大学博士学位论文，2000．
2 在英语中，共同基金mutual fund的mutual意为joint(联合)，而fund有holding(控制)之意，即把许多人的钱集中起来进行专业化投资的运作。共同基金其实就是一类投资公司investment company。作为公司，每一个共同基金都有各自不同的经理、员工、运作方式和目标等等。基金的投资目标反映其成立和存在的理由。简而言之，共同基金集合了一部分委托人的资金，并代表他们的利益进行有预设目的投资。每个基金公司都会雇佣投资专业人士来管理基金的投资组合，通常称他们为投资组合管理者。这些专业人士可以组成团队来经营基金，有些投资公司甚至委托其他公司或自由投资专业人士来帮助公司进行资本运作。
3 从2006年11月开始，已经有封闭式基金陆续到期，有的已经进入封转开进程，而有的可能面临清盘的命运。从而使得我国封闭式基金总体规模呈现出逐渐缩小的趋势。
4 由于开放式基金较晚引入我国，所以2002年以前国内发表的证券投资基金研究成果基本是针对封闭式基金进行的。此后，国内依然有大量关于封闭式基金的研究涌现出来。
5 参见Poon和Granger(2003)对国外主要波动研究模型的分类。
6 参见Engle R F, Risk and Volatility: Econometric Models and Financial Practice[J]. American Economic Review, 2004, 94(3): 405-420.
7 对不同市场间收益波动的相互影响更为流行的称谓应当是“联动”(Co-movement)。但鉴于国内有的实证文章中把指数间的关联性也称为联动，为避免混淆，本文将指数间收益波动的相互影响统称为“波动关系”。
1 该5条标准由计量经济学家A．C．Harvey提出，参见Harvey,A．C．Economic Analysis of Time Series[M]，Wiley，New York，1981，p5-7．
2 Friedman N, Geiger D, Goldszmidt M. Bayesian network classifiers [J]. Machine Learning,1997, 29: 131～161.
3 根据统计学知识，正态分布的偏度为0，峰度为3。若序列的统计特征显著偏离上述数值，则认为数据不服从正态分布。
4 参见王春峰．VaR金融市场风险管理[M]．天津：天津大学出版社，2001．
5 参见高铁梅．计量经济分析方法与建模：EVIEWS应用及实例[M]．清华大学出版社2006，P175-176．
6 参见高铁梅．计量经济分析方法与建模：EVIEWS应用及实例[M]．清华大学出版社2006，P128．
7 关于长记忆波动模型的介绍可参见张世英，樊智．协整理论与波动模型——金融时间序列分析及应用[M]．北京：清华大学出版社，2004．
8 由于对角多元GARCH简化了多个变量之间的相关关系，因而该模型无法用于研究多个市场间的波动相互关系和溢出效应。
9 Berndt, E. R, Hall, B. H., Hall, R. E and Hausman. Estimation and inference in nonlinear structural models. Annual of Economic and Social Measurement, 1974(4): 653-665.
1 参见蔡(Tsay)著《金融时间序列分析》中译本，机械工业出版社，2006版第73页。
2 以该办法的出台为分水岭，此前的基金成为老基金，此后上市的基金品种成为新基金。
3 称其为新基金是相对于98年前成立的老基金而言。
4 截至2006年12月我国封闭式基金尚有53只。基金兴业(sh5000283已经与2006年底成功转为开放式基金——华夏稳健增长，成为国内首家基金封转开的成功案例。
5 从模型估计结果看，针对单只封闭式基金的GARCH-GED模型建模效果均不够理想，所以此处采用t分布与GARCH模型组合进行估计。下文中针对单只封闭式基金的EGARCH模型建模采用t分布假设基于相同的考虑。
6 与GARCH模型的情况类似，从模型估计结果看，针对单只封闭式基金的EGARCH-GED模型建模效果均不够理想，所以此处采用t分布与EGARCH模型组合进行估计。
7 其他主要的日历异常有一月效应，月度更替效应和假日效应。
8 大量研究表明，证券收益率的分布普遍具有尖峰厚尾现象。本文第3章已经阐明我国基金市场的收益同样存在尖峰厚尾特征。
1 如本文第三章所述，封闭式基金指数的收益序列均没有显著的自相关，收益围绕一个均值水平波动，可以采用简单的均值方程设定。当然，研究中可以假定条件均值服从ARMA过程，然而这种假定会使得模型的估计更为复杂。根据Nelson(1990)，均值方程中滞后阶数的选择对条件方差方程的参数估计不产生实质影响，因此这里同样选取简化方程形式。
2 关于GARCH族类模型滞后阶数的选取尚缺乏为大家共同接受的标准，以往的研究一般认为GARCH项和ARCH项滞后阶数都取为1便足以描述金融市场的波动状况。例如，Lamoureux等(1993)认为GARCH(1，1)或者EGARCH(1，1)形式能够很好地评估条件方差，其他支持GARCH(1，1)形式的证据可见汉密尔顿(1994)。如下文所述，向量GARCH(1，1)形式对于我国市场也是合适的。
3 参见黄大海2004。另外，由于提高多元GARCH模型的滞后阶数会极大的增加模型中参数个数，增加估计的困难程度。并且在一元GARCH建模中，一般选取形式最为简单的GARCH(1，1)便可以取得较好的建模效果。所以，这里选择BEKK(1，1)模型来进行研究。
1 截至2006年6月，我国封闭式基金的发行份额为817亿，远远小于股市规模。
2 此处均值方程中包含的是条件标准差的一阶之后，与上证综合指数的均值方程设定不同。
3 根据定义，协整检验(Co-integration Test)只能在同阶的非平稳序列间进行。
[1] Andersen T G, Bollerslev T. Answering the critics: yes, ARCH models do provide good volatility forecasts[J]. International Economic Review, 1998, 39(4): 885-905.
[2] Baillie R T. Long memory processes and fractional integration in econometrics [J]. Journal of Econometrics, 1996, 73(1): 5-59.
[3] Baillie, R. T, Bollerslev, T. Mikkelsen, H. O. Fractionally integrated generalized autoregressive conditional heteroskedasticity[J]. Journal of Econometrics, 1996, (74): 3-30.
[4] Banerjee, A., Dolado, J. J., Hendry, D. F., Smith, G. W. Exploring equilibrium relationships in econometrics through static models: Some monte carlo Evidence[J]. Oxford Bulletin of Economics and Statistics, 1986, (48): 253-277.
[5] Banerjee, A., Hendry, D. F. Testing Integration and cointegration: An overview[J]. Oxford Bulletin of Economics and Statistics, 1992, (54): 225-255.
[6] Bekart, Harvey, Bekart, G., Harvey, C. R. Emerging equity market volatility[J]. Journal of Financial Economics, 1997, (43): 29-77.
[7] Bera, A. K., M. L. Higgins. A test for conditional heteroskedasticity in time series models[J]. Journal of Time Series Analysis, 1992, (13): 501-19.
[8] Bera A. K., Higgins M. L. ARCH Models: Properties, Estimation and Testing[J]. Journal of Economic Surveys, 1993, (7): 305-366.
[9] Berndt E K, et al. Estimation inference in nonlinear structural models[J]. Annals of Economic and Social Measurement, 1974, (4): 653-665.
[10] Berndt, E. R, Hall, B. H., Hall, R. E and Hausman. Estimation and inference in nonlinear structural models[J]. Annual of Economic and Social Measurement, 1974, (4): 653-665.
[11] Bollerslev T, et al. A capital-asset pricing model with time-varying coefficients [J]. Journal of Political Economy, 1988, (96): 116-131.
[12] Bollerslev T, Mikkelsen H. O. Modeling and pricing long memory in stock market volatility[J]. Journal of Econometrics, 1996, (73): 151-184.
[13] Bollerslev, T., Engle R. F., Wooldridge, J. M., A capital asset pricing model with time-varying covariance[J], Journal of Political Economy, 1988, (96): 116-131.
[14] Bollerslev T. Modeling the coherence in short_run nominal exchange rates: A multivariate generalized ARCH model[J]. The Review of Economics and Statictics, 1990, (72): 498-505.
[15] Bollerslev, T., R. Y. Chou, K. F. Kroner. ARCH Modeling in Finance: A Review of the Theory and Empirical Evidence[J]. Journal of Econometrics, 1992, (52): 5-59.
[16] Bollerslev, Tim. Generalized Autoregressive Conditional Heteroskedasticity [J]. Journal of Econometrics, 1986, (31): 307-327.
[17] Boswijk, H. P.. Estimation and testing for cointegration with trended variables: A comparison of a static and a dynamic regression procedure[R]. Netherland: University of Amsterdam, 1989.
[18] Bollerslev, T., R. Y. Chou, and K. F. Kroner. ARCH Modeling in Finance: A Review of the Theory and Empirical Evidence[J]. Journal of Econometrics, 1992, (52): 5-59.
[19] Bollerslev, T., Robert F. Engle and Daniel B. Nelson. 1994. "ARCH models" in Handbook of Econometrics[M]. Vol. Ⅳ. Robert F. Engle and D. L. McFadden, eds. Amsterdam: North-Holland, 2959-3038.
[20] Boswijk, H. P. Testing for cointegration in structural models[R]. Netherland: University of Amsterdam, 1991.
[21] Brailsford, Timothy J. and Robert TV, Faff. An evaluation of volatility forecasting techniques[J]. Journal of Banking and Finance, 1996, (20): 419-438.
[22] Breidt, F. J.. A Threshold Autoregressive Stochastic Volatility Model[J]. Iowa State University Working paper, 1996.
[23] Brooks C,. A double Threshold GARCH Model for the French Frank/German Mark Exchange Rate Journal of Forecasting, 2001, (20): 135-143.
[24] Cao, Charles Q. and Ruey S. Tsay. Nonlinear Time-Series Analysis of Stock volatilities[J]. Journal of Applied Econometrics, 1992, (1): 165-85.
[25] Chiang, T. C., Doong, S.. Empirical analysis of stock returns and volatility: Evidence from Seven Asian Stock markets based on TAR-GARCH model[J]. Review of Quantitative Finance and Accounting, 2001, (17): 301-318.
[26] Clare Andrew, Ian Garrett, Greg Jones. Testing for seasonal patterns in conditional return volatility: evidence fromAsia-Pacific markets[J]. Applied Financial Economics, 1997, (7): 517-523.
[27] Clark P K. A subordinated stochastic process model with finite variance for speculative prices[J]. Econometrica, 1973, (41): 135-155.
[28] Cross F. Price movements on fridays and mondays[J]. Financial Analysts Journal, 1973, 29(6): 67-69.
[29] DeBondt, W. F. M. and R. H. Thaler. Does the stock marketoverreact? [J]. Journal of Finance, 1985, (40): 793-808.
[30] Dickey, D. A. and W. A. Fuller. Distribution of the Estimators for Autoregressive Time Series with a Unit Root[J]. Journal of the American Statistical Association, 1979, (74): 427-431.
[31] Diebold, Francis X. and Jose A. Lopez. 1995. "Modelling volatility Dynamics" in Macroeconomics: Developments[M], Tensions and Prospects. Kevin D. Hoover, ed. Boston, Dordrecht, and London: Kluwer Academic, 427-466.
[32] Ding Zhuangxin. C. W. J, Granger, and R. F. Engle. ALong Memory Property of Stock Market Returns and A New Model[J]. Journal of Empirical Finance, 1993, (1): 83-106.
[33] Dirk Baur and Robert Jung. Return and volatility linkages between the US and the German stock market[J]. Journal of International Money and Finance 2006(25): 598-613.
[34] Engle, R. F, Kroner K F. Multivariate simultaneous generalized ARCH [J]. Econometric Theory, 1995, (11): 122-150.
[35] Engle, R. F. Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of U. K. Inflation[J]. Econometrica, 1982, (50): 987-1008.
[36] Engle, R. F. and V. K. Ng. Measuring and Testing the Impact of News on Volatility[J]. Journal of Finance, 1993, (48): 1749-1778.
[37] Engle, R. F. & C. W. J. Granger. Co-integration and error correction: representation, estimation andtesting[J]. Econometrica, 1987, (55): 251-276.
[38] Engle, R. F. and Kraft, D. F. Multi period Forecast Error Variances of Inflation Estimated from ARCH Models[M]. Applied Time Series Analysis of Economic Data, 1983: 293—302.
[39] Engle R. F., Lilien D. M., and Robins R. P.. Estimating time varying risk premia in the term structure: the ARCH-M model[J]. Econometrica, 1987, 55: 391-407.
[40] Engle, R. F.. Risk and Volatility: Econometric Models and Financial Practice[J]. American Economic Review, 2004, 94 (3): 405-420.
[41] Engle, R. F., Takatoshi Ito, and Wen-Ling Lin. Meteor Showers or HeatWawes? Heteroskedastic Intra-Daily Volatility in the Foreign Exchange Market[J], Econometrica, 1990, (58): 525.
[42] Engle R. F. and Susmel R., 1993, Common volatility in international equality markets, Journal of business and economics statistics, 1993, (11): 167-176.
[43] Fama, Eugene F. Stock Returns, Real Activity, Inflation, and Money[J]. American Economic Review, 1990, (71): 545-565.
[44] Fama, Eugene F. The Behavior of Stock Market Price[J]. Journal of Business, 1965, (38): 34-105.
[45] Fang, W. The effects of currency depreciation on stock returns: Evidence from five East Asian economies[J]. Applied Economics Letters, 2002, (9): 195-199.
[46] Flannery, M., Protopapadakis, A.. Macroeconomic factors do Influence aggregate stock returns[J]. Review of Financial Studies, 2002, (15): 751-782.
[47] Forbes, Kristin J. and Roberto Rigobon. No Contagion, Only Interdependence: Measuring Stock Market Comovements[J]. Journal of Finance, 2002, (57): 2223-2261.
[48] French K R, Schwert G W, Stambaugh R F. Expected stock returns and volatility[J]. Journal of Financial Economics, 1987, (19): 3-29.
[49] Friedman N, Geiger D, Goldszmidt M. Bayesian network classifiers[J]. Machine Learning, 1997, (29): 131-161.
[50] Fuller, W. A. Introduction to statistical time series[M]. New York: John Wiley and Sons, 1976.
[51] Gibbons M, Hess P. Day of the week effects and asset returns [J]. Journal of Business, 1981, 54(5): 579-596.
[52] Glosten, L. R., Jagannathan, D. Runkle. On the Relation Between the Expected Value and the Volatility of the Nominal Excess Return on Stocks[J]. Journal of Finance, 1993, (48): 1779-1801.
[53] Granger, C. W. J. Investigating Causal Relations by Econometric Models and Cross-Spectral Methods[J]. Econometrica, 1969, 37 (3): 424-38.
[54] Granger, C. W. J., Machina, M. J. Structural Attribution of Observed Volatility Clustering[J]. Journal of Econometrics, 2006, (135): 15-29.
[55] Gray S. F. Modeling the Conditional Distribution of Interest Rates as a Regime-switching Process[J]. Journal of Financial Economics, 1996, (42): 27-62.
[56] Ghysels, Eric; Andrew Harvey and Eric Renault. "Stochastic Volatility," in Handbook of Statistics: Statistical Methods in Finance, Vol. 14. G.S. Maddala and C. R. Rao, eds. Amsterdam: Elsevier Science, 1996. 119-191.
[57] Hamao, Y., Masulis, R. W. Ng, V. Correlationsin Price Changes and Volatility across International Stock Markets[J]. Review of Financial Studies, 1990, (3): 281-307.
[58] Harvey A. C., Shephard N. The estimation of an asymmetric stochastic volatility model for asset returns [J]. Journal of Business and Economic Statistics, 1996, (14): 429-434.
[59] Harvey A. C., E. Ruiz and N. Shephard. Multivariate Stochastic Volatility Models[J]. Review of Economic Studies, 1994, (61): 247-264.
[60] Harvey, A. C. Economic Analysis of Time Series[M]. New York: Wiley, 1981: 5-7.
[61] Harvey C R, Whaley R E. Market volatility prediction and the efficiency of the S&P100 index option market. Journal of Financial Economics, 1992, (31): 43-74.
[62] Heynen, Ronald C. and Hany M. Kat. Volatility Prediction: A Comparison of Stochastic Volatility, GARCH(1,1) and EGARCH(1,1)Models[J]. Journal of Deriuatiues 1994, (2): 50-65.
[63] Ho Y. K., Cheung Y. L. Seasonal pattern in volatility in Asian stock markets[J]. Applied Financial Economics, 1994, (4): 61-67.
[64] Hull, John and Alan White. The pricing of options on assets with stochastic volatilities[J]. Journal of Finance, 1987, 42(2): 281-300.
[65] Jacquier, E. Polson, N. G, Rossi, P. E. Bayesian analysis of stochastic volatility models[J]. Journal of Business and Economic Statistics, 1994, (12): 371-389.
[66] Jeffrey Pontiff. Excess Volatility and Closed-End Funds[J]. The American Economic Review, 1997, 87(1): 155-169.
[67] Jeon, B. N., Von Furstenberg, G. M., 1990. Growing international co-movement in stock price indexes[J]. Quarterly Review of Economics and Business(30): 15-30.
[68] Johansen S. J. Testing weak exogeneity and the efficiency of single equation analysis[J]. Journal of Econometrics, 1992, (52): 389-402.
[69] Johansen, Soren. Estimation and Hypothesis Testing of Cointegration Vectors in Gaussioan Vector Autoregressive Models[J]. Econometrica, 1991, (59): 1551-1580.
[70] Johansen, Soren. Likelihood-based Inference in Cointegrated Vector Autoregressive Models[M]. Oxford: Oxford University Press, 1995.
[71] Johnsen, S. J. Statistical analysis of cointegration vectors[J]. Journal of Economic Dynamics and Control, 1988, (12): 231-254.
[72] Johnsen, S. J. and Juselius, K. Maximum likelihood estimation and inference on cointegration—with Application to the demand for money[J]. Oxford Bulletin of Economics and Statistics, 1990, (52): 169-210.
[73] Kearney, C., Andrew Patton, Multivariate GARCH modeling of exchange rate volatility transmission in the European monetary system[J], The Financial Review, 2000, (41): 29-48.
[74] Kim S. Shephard N. Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models[R]. Nuffield College, London: Oxford University, 1994.
[75] King, Mervyn and Sushil Wadhwani., Transmission of Volatility between Stock Markets[J]. Review of Financial Studies, 1990, (3): 5-33.
[76] King, Mervyn, Enrique Sentana and Sushil Wadhwani. Volatillity and Links between National Stock Markets[J]. Econometrica, 1994, (62): 901-933.
[77] Klaassen, F. Improving Garch Volatility Forecasts with Regime-Switching GARCH. Empirical Economics, 2002, (27): 363-394.
[78] Koopman S J, Uspensky E H. The stochastic volatility in mean model: Empirical evidence from international stock markets[J]. Journal of Applied Econometrics, 2002, (17): 667-689.
[79] Lee, Keun Yeong. Are the GARCH Models Best in Out-Of-Sample Performance? [J]. Economic Letters, 1991, 37(3): 305-308.
[80] Li, Q. et al. The relationship between stock returns and volatility in international stock markets[J]. Journal of Empirical Finance, 2005, (12): 650-665.
[81] Lin, W., Engle, R. F. Ito, T. Do Bulls and Bears Move across Borders? International Transmission of Stock Returns and Volatility[J]. Review of Financial Studies, 1994, (7): 507-538.
[82] Mandelbrot. The variation of certain speculative prices[J], Journal of Business, 1963, (36): 394-419.
[83] Merville, L. H., and D. R. Pieptea. Stock Price Volatility, Mean-Reverting Diffusion, and Noise[J]. Journal of Financial Economics, 1989, (24): 193-214.
[84] Michael J. Dueker. Markov Switching in GARCH Processes and Mean-Reverting Stock-Market Volatility[J]. Journal of Business & Economic Statistics, 1997, (15); 26-34.
[85] Nelson, Daniel B. Conditional Heteroskedasticity in Asset Returns: A New Approach[J]. Econometrica, 1991, (59): 347-370.
[86] Ng, V. K., Chang, R. P. and Chou, R. Y. An examination of the behaviour of Pacific-Basin stock market volatility[J], Pacific-Basin Capital Markets Research, 1991, (2): 245-260.
[87] Nicholls, D. F., and B. G. Quinn. Random Cofficient Autoregressive Models: An Introduction[M]. New York: Lecture Notes in Statistics, 1999: 11.
[88] Nikkinen, J. et al. Global stock market reactions to scheduled U. S. macroeconomic news announcements[J]. Global Finance Journal, 2006, (17): 92-104.
[89] Pagan, A. R., and G. W. Schwert. Alternative Models for Conditional Stock Volatility[J]. Journal of Econometrics, 1990, (45): 267-290.
[90] Phillips, P. C. B. Time series regression with a unit root[J]. Econometrica, 1987, (55): 277-301.
[91] Phillips, P. C. B. Towards a unified asymptotic theory for autoregression [J]. Biometrika, 1987, (74): 535-547.
[92] Phillips, P. C. B. Regression theory for near-integrated time series[J]. Econometrica, 1988, (56): 1021-1043.
[93] Phillips, P. C. B. and P. Perron. Testing for a Unit Root in Time Series Regression[J]. Biometrika, 1988, (75): 335-346.
[94] Poon S-H, Granger C. Forecasting volatility in financial markets: a review[J]. Journal of Economic Literature, 2003, (41): 478-539.
[95] Poterba, James M and Lawrence H. Summers. The persistence of volatility and stock market fluctuations[J]. American Economic Review, 1986, (76): 1142-1151.
[96] Robert J. Shiller. Comovements in Stock Prices and Comovements in Dividends[J]. The Journal of Finance, 1989, 44(3): 719-729.
[97] Robert J. Shiller. Market Volatility and Investor Behavior[J]. The American Economic Review, 1990, 80(2): 58-62.
[98] Schwert, W. G. Why does stock market volatility change over time?[J]. Journal of Finance, 1989, (44): 1368-1388.
[99] Sheikh A M. The behavior of volatility expectations and their effects on expected returns. Journal of Business, 1993, (66): 93-116.
[100] Smith, Daniel R. Markov-Switching and Stochastic Volatility Diffusion Models of Short-Term Interest Rates[J]. Journal of Business & Economic Statistics, 2002, 20(2): 183-197.
[101] So M. K., and Lain, K., and Li, W. K. A stochastic volatility model with Markov switching[J]. Journal of Business& Economic Statistics, 1998, (16): 244-253.
[102] Tauchen G., Pitts M. The price variability-volume relationship on speculative markets[J]. Econometrica, 1983, (51): 485-505.
[103] Taylor S. Modeling financial time series[M]. New York: Wiley and Sons, 1986.
[104] Tsay, R. S. Conditional Heteroscedastic Time Series Models[J]. Journal of the American Statistical Association, 1987, (82): 590-604.
[105] Wang, P., A. Liu, Return and risk interaction in Chinese stock markets, Journal of International Financial Markets[J], Institutions & Money, 2004, (14): 367-384.
[106] Zakoian, J. M. Threshold heteroskedasticmodels[J]. Journal of Economic Dynamics and Control, 1994, (18): 931-955.
[107] Zakoian, J. M. Threshold HeteroskedasticityModels[M]. Paris: Manusctipt, CREST, INSEE, 1990.
[108] 蔡(Tsay)著，潘家柱译．金融时间序列分析[M]．机械工业出版社，2006．
[109] 陈娟，沈晓栋．中国股票市场收益率与波动性的阶段性研究[J]．统计与决策，2005，(08)：98-100．
[110] 陈浪南，黄杰鲲．中国股票市场波动非对称性的实证研究[J]．金融研究，2002，(5)：67-73．
[111] 陈梦根．中国证券市场下一步发展的路径选择[J]．财贸经济，2004，(8)：62-67．
[112] 陈千里．股市波动的周内效应研究[J]．湖北大学学报(自然科学版)，2003，(1)：29-32．
[113] 陈学彬，傅东升，葛成杰．我国居民个人生命周期消费投资行为动态优化模拟研究[J]．金融研究，2006，(2)：21-35．
[114] 陈永新．中国股市与宏观经济形势的背离及原因分析[J]．云南财贸学院学报，2005，21(2)：34-38．
[115] 樊智，张世英．多元GARCH建模及其在中国股市分析中的应用[J]．管理科学学报，2003，(4)：68-73．
[116] 奉立城．中国股票市场的“周内效应”[J]．经济研究，2000，(11)：50-56．
[117] 高铁梅．计量经济分析方法与建模：EVIEWS应用及实例[M]．北京：清华大 学出版社，2006．
[118] 郭晓亭，林略．证券投资基金风险的表现形式及特征[J]． 商业研究，2004，(23)：138-140．
[119] 郭晓亭．中国证券投资基金市场波动特征实证研究[J]．中国管理科学，2006，(01)：15-20．
[120] 贺京同，沈洪溥．基于行为金融学的我国基金价格波动研究[J]．经济学动态，2002，(6)：42-46．
[121] 洪永淼、成思危等．中国股市与世界其它股市之间的大风险溢出效应[A]．见：2004年中国虚拟经济年会．天津：2004．
[122] 胡国军．中国证券市场波动率的实证研究[D]．上海：复旦大学硕士学位论文，2005．
[123] 黄炳艺，曾五一．证券投资基金市场与股票市场运行特征实证分析[J]．天津财经学院学报，2004，(5)：23-25．
[124] 黄大海．中国股票市场价格波动的理论与实证研究[D]．天津大学，2004．
[125] 蒋璐．股票价格波动与货币政策的关联性研究——我国1997-2004年的实证分析[D]．上海：复旦大学硕士学位论文，2005．
[126] 金洪飞．上海股市(1997．7-2003．5)的波动性、风险溢价和周末效应分析[J]．清华大学中国经济研究中心工作论文，2005．
[127] 金晓斌，高道德，石建民，刘红忠．中国封闭式基金折价问题实证研究[J]．中国社会科学，2002，(5)：55-66．
[128] 李峰．不同波动特性下金融市场风险计量与规避[D]．长沙：湖南大学硕士学位论文，2003．
[129] 李汉东，张世英．ARCH模型与SV模型之间关系研究[J]．系统工程学报，2003，18(2)：97-103．
[130] 李子奈，叶阿忠．高等计量经济学[M]．北京：清华大学出版社，2000．
[131] 刘金全，崔畅．中国沪深股市收益率和波动性的实证分析[J]．经济学(季刊)，2002，(1)：885-898．
[132] 刘晓星，何建敏，刘庆富．基于VaR-EGARCH-GED模型的深圳股票市场波动性分析．南开管理评论，2005，(8)：9-13．
[133] 陆慰祖．高等时间序列经济计量学[M]．上海：上海人民出版社，1999．
[134] 罗洪浪，王浣尘．封闭式基金的超额波动性研究[J]．证券市场导报，2003，(10)．
[135] 马超群，陈牡妙．金融系统ARCH模型及应用[J]．湖南大学学报，1998，(5)：108-112．
[136] 马超群，傅安里，杨晓光．中国投资基金波动择时能力的实证研究[J]．中国管理科学，2005，(4)：22-28．
[137] 孟祥友．封闭式基金市场波动特征研究[D]．成都：西南财经大学，2006．
[138] 莫扬．股票市场波动性的国际比较研究[J]．数量经济技术经济研究，2004，(10)：49-56．
[139] 牛方磊，卢小广．基于ARCH类模型的基金市场波动性研究[J]．统计与决策，2005，(24)：109-110．
[140] 牛方磊．我国封闭式基金市场关联性与价格波动研究[D]．河海大学，2006．
[141] 祁斌．机构投资者发展与市场有效性及稳定性研究中国证券网[N]．上海证券报，2005，12(23)．
[142] 钱争鸣．ARCH族计量模型再金融市场研究中的应用[J]． 厦门大学学报(哲学社会科学版)，2000，(3)：126-129．
[143] 沈洪溥，周立群．行为金融学对封闭式基金价格波动的解释[J]．金融教学与研究，2003，(5)：41-43．
[144] 宋逢明，李翰阳．中国股票波动性的分解实证研究[J]．财经论丛，2004，(7)：23-28．
[145] 唐齐鸣，陈健．中国股市的ARCH效应分析[J]．世界经济，2001，(3)：29-36．
[146] 汪素南，潘云鹤．美国股市与中国股市间溢出效应的实证研究[J]．浙江大学学报(工学版)，2004，38(11)：1431-1435．
[147] 王春峰．VaR金融市场风险管理[M]．天津：天津大学出版社，2001．
[148] 王明涛等．证券市场风险测度——管理模型研究[M]．上海：上海财经大学出版社，2006．
[149] 王明照，郭冰．金融资产波动性特征研究回顾[J]．经济学动态，2005，(1)：76-81．
[150] 王新宇，宋学锋，吴瑞明．中国证券市场市场的分形分析[J]．管理科学学报，2004，7(5)：67-74．
[151] 温素彬．我国股市波动的ARCH类模型分析[J]．淮海工学院学报，2002，(2)：64-67．
[152] 许承明，宋海林．中国封闭式基金价格报酬过度波动的经验分析[J]．经济研究，2005，(3)：108-118．
[153] 杨湘豫，周屏．GARCH模型在开放式基金中的实证研究[J]．系统工程，2006，(04)：73-76．
[154] 殷玲，唐杰．GARCH-M模型与我国沪深股市的波动[J]．江南大学学报，2002，(4)：56-58．
[155] 詹姆斯D．汉密尔顿．时间序列分析[M]．北京：中国社会科学出版社，1999．
[156] 张俊生．中国封闭式证券投资基金折价交易的实证分析[J]．经济科学，2001，(2)：50-55．
[157] 张世英，柯珂．ARCH模型体系[J]．系统工程学报，2002，(3)：236-245．
[158] 张世英，樊智．协整理论与波动模型——金融时间序列分析及应用[M]：北京：清华大学出版社，2004．
[159] 张晓峒．计量经济分析[M]．北京：经济科学出版社，2001．
[160] 张宗新．经济转型期证券资产价格波动的制度解析[J]．数量经济技术经济研究，2005，(1)：18-33．
[161] 周俊，中国股票市场价格波动数量分析[D]．成都：西南财经大学博士学位论文，2000．