基于SHIBOR的波动率研究
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摘要
金融资产收益的波动率研究是当代金融经济学和计量经济学研究的核心领域,作为市场风险的度量,对波动率的辨识将直接影响到资产定价、资源配置以及风险管理的建模。一方面,波动率与市场的不确定性和风险直接相关,波动性增加,表示风险的增加,高风险将对企业经营造成的冲击较大,而收益—风险的均衡是资本配置与资产定价的核心;另一方面,波动率与反映股票市场质量和效率的其他指标密切相关,这些指标包括流动性、透明性、交易成本、市场效率、信息流动特性等,它们是最简洁和最有效的综合反映股市价格行为、质量和效率的指标之一。除此之外,波动率对企业财务杠杆、营运杠杆和投资杠杆的决策,对个人的消费与投资行为,以及对经济周期和相关宏观经济变量等都有着不可低估的作用。正因为如此重要,波动率成为金融市场不变的主题。
本文针对我国货币市场基准利率的波动进行研究,选取上海银行间同业拆放利率的隔夜、一周、三月及一年利率日数据作为研究样本。首先介绍了波动理论及各种波动估计模型,主要介绍了GARCH族模型。接着分析了选取利率序列的统计特征。从统计特征图和QQ图可看出,SHIBOR市场各期限利率序列不服从正态分布,短期序列具有尖峰厚尾特征;长期序列一阶平稳,无ARCH效应。然后借助五种GARCH类模型对上海银行间同业拆放短期利率各个方面的波动特征进行实证研究,得出模拟SHIBOR隔夜利率序列及一周利率序列波动最佳的模型。实证结果显示,在GARCH族模型(EGARCH除外)拟合下,本文所选取利率序列不存在条件异方差和残差序列相关现象,模型的拟合效果很好。基于正态分布的SHIBOR利率序列方差方程存在明显的非对称效应。从文中采用的模型来看,根据AIC和SC信息准则,无论是SHIBOR隔夜利率序列还是一周利率序列,PGARCH拟合下的方差方程都比其他模型的方差方程的拟合效果要好。最后对本文进行了总结与展望,为今后进一步深化对我国货币市场风险特征的研究提供一定的参考。
Analysis on the volatility of financial assets is the nucleus realm of the research of contemporary financial economics and econometrics. As the generous character of market risk, the identification of volatility will have a direct impact on assets pricing, resource arrangement and the model of venture management. On one hand, the volatility is directly related to uncertainties and risks of the market, and the core of asset pricing and capital configuration is the balance between benefits and risk; on the other hand, it closely related to other indicators which reflect the quality and efficiency of the stock market such as liquidity, market efficiency, transaction costs, transparency and information flowing characteristics, is as one of the most simple and effective indicator that can comprehensively reflect the price behavior, quality and efficiency of stock market. In addition, the volatility has an important influence on the decision to business investment leverage, financial leverage and operating leverage, on personal consumption and investment behavior, and on the economic cycle and the relevant macroeconomic variables. Because of its importance, volatility becomes a constant theme of financial market.
This paper is a research on the volatility of benchmark interest rate in currency market, selecting the data of SHIBOR for overnight, one-week, one-season and one-year interest rate as the study sample. Firstly the author introduces the wave theory and a variety of volatility estimation model, main part is the GARCH models. Then author analyzes the statistical characteristics of selected sequences of interest. Seen from the statistical characteristical maps and QQ plans, SHIBOR term market interest rate sequence does not obey the normal distribution, bringing about a short sequence with a fat tail and long sequence of first-order stationary, no ARCH effects. And then various aspects in short-term interest rate characteristics have empirically researched with five GARCH type models, obtained the best model on simulation SHIBOR overnight rate sequences and sequences of the week fluctuations in interest rates. The empirical results show that in the GARCH models (EGARCH excluded), sequence of interest rates which the paper selected does not exist phenomenon of the conditional heteroskedasticity and residual sequence, actually the model fit very well. SHIBOR Interest rate sequence variance equation based on normal distribution exist obvious asymmetric effect. From the model used in the text view, according to AIC and SC information criteria, whether it is SHIBOR interest rates overnight rate series or sequence of the week, the variance equation under PGARCH fits better than that of other models . Finally, this paper make a summary and give prospects for future, providing some reference for further study of deepening the money market risk characteristics.
本文针对我国货币市场基准利率的波动进行研究,选取上海银行间同业拆放利率的隔夜、一周、三月及一年利率日数据作为研究样本。首先介绍了波动理论及各种波动估计模型,主要介绍了GARCH族模型。接着分析了选取利率序列的统计特征。从统计特征图和QQ图可看出,SHIBOR市场各期限利率序列不服从正态分布,短期序列具有尖峰厚尾特征;长期序列一阶平稳,无ARCH效应。然后借助五种GARCH类模型对上海银行间同业拆放短期利率各个方面的波动特征进行实证研究,得出模拟SHIBOR隔夜利率序列及一周利率序列波动最佳的模型。实证结果显示,在GARCH族模型(EGARCH除外)拟合下,本文所选取利率序列不存在条件异方差和残差序列相关现象,模型的拟合效果很好。基于正态分布的SHIBOR利率序列方差方程存在明显的非对称效应。从文中采用的模型来看,根据AIC和SC信息准则,无论是SHIBOR隔夜利率序列还是一周利率序列,PGARCH拟合下的方差方程都比其他模型的方差方程的拟合效果要好。最后对本文进行了总结与展望,为今后进一步深化对我国货币市场风险特征的研究提供一定的参考。
Analysis on the volatility of financial assets is the nucleus realm of the research of contemporary financial economics and econometrics. As the generous character of market risk, the identification of volatility will have a direct impact on assets pricing, resource arrangement and the model of venture management. On one hand, the volatility is directly related to uncertainties and risks of the market, and the core of asset pricing and capital configuration is the balance between benefits and risk; on the other hand, it closely related to other indicators which reflect the quality and efficiency of the stock market such as liquidity, market efficiency, transaction costs, transparency and information flowing characteristics, is as one of the most simple and effective indicator that can comprehensively reflect the price behavior, quality and efficiency of stock market. In addition, the volatility has an important influence on the decision to business investment leverage, financial leverage and operating leverage, on personal consumption and investment behavior, and on the economic cycle and the relevant macroeconomic variables. Because of its importance, volatility becomes a constant theme of financial market.
This paper is a research on the volatility of benchmark interest rate in currency market, selecting the data of SHIBOR for overnight, one-week, one-season and one-year interest rate as the study sample. Firstly the author introduces the wave theory and a variety of volatility estimation model, main part is the GARCH models. Then author analyzes the statistical characteristics of selected sequences of interest. Seen from the statistical characteristical maps and QQ plans, SHIBOR term market interest rate sequence does not obey the normal distribution, bringing about a short sequence with a fat tail and long sequence of first-order stationary, no ARCH effects. And then various aspects in short-term interest rate characteristics have empirically researched with five GARCH type models, obtained the best model on simulation SHIBOR overnight rate sequences and sequences of the week fluctuations in interest rates. The empirical results show that in the GARCH models (EGARCH excluded), sequence of interest rates which the paper selected does not exist phenomenon of the conditional heteroskedasticity and residual sequence, actually the model fit very well. SHIBOR Interest rate sequence variance equation based on normal distribution exist obvious asymmetric effect. From the model used in the text view, according to AIC and SC information criteria, whether it is SHIBOR interest rates overnight rate series or sequence of the week, the variance equation under PGARCH fits better than that of other models . Finally, this paper make a summary and give prospects for future, providing some reference for further study of deepening the money market risk characteristics.
引文
[1] Engle,R.F. Autoregressive Conditional heteroskedasticity with Estimates of the Variance of the United Kingdom Inflation. Econometrica,1982,(50):987-1007.
[2] Taylor S. Modelling Finaneial Time Series. New York:Chichester, John Wiley and Sons,1986
[3] Andersen,T.G. and T.Bollerslev. Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts. International Economic Review,1998,(39):885-905.
[4] Sehwert,W.G. Why does stock market volatility change over time. Journal of Finanee,1989,(44):1368-1388
[5] Campbell,J.Y., Grossman,S.J., Wang J. Trading volume and serial correlation in stock returns. Wuarterly Journal of Economies, 1993,(108):905-939
[6] Nakagawa, Shinobu, NaotoOsawa. Financial Market and Macroeconomic Volatility- -Relationships and Some Puzzles. Bank of Japan Working Paper,2000,00-9
[7] Mandelbrot B. The variation of some other speculative prices.In:P.Cootner,ed.The Random Character of Stoek Prices. Cambridge, MA:M.I.T.Press,1964
[8] Fama E. The Behavior of Stock Market Prices. Journal of Business,1965,(l),34-105
[9] Bollerslev T. Generalized autoregressive conditional heterskedasticity. Journal of Econometrics,1986,(31):307-327.
[10] Engle,R.F., Lilien,D.M. and Robins,R.P. Estimating time-varying risk premia in the term structure: The ARCH-M model. Econometrica, 1987,(55):391-407
[11] Fischer Black. The Dividend Puzzle. Journal of Portfolio Management, Winter 1976:5-8
[12] Nelson,D.B. ARCH models as diffusion approximations. Journal of Econometrics, 1990,(45):7-38
[13] Zakoian,J.M. Threshold heteroskedastic models. Journal of Economic Dynamics and Control,1990,(18):931-955
[14] Ding,Z., Granger,C.W.J. and Engle,R.F. A long memory property of stoek market returns and a new model. Journal of Empirical Finanee,1993,(l):83-106
[15] Ding,Z. and Granger,C.W.J. Modeling volatility persistence of spceulative returns:A new approach. Econometrics.1996,(73):185-215
[16] Engle R,Lee G. A permanent and transitory component model of stoek return volatility. Department of Economics,UCSD,1993
[17] Hull J. and White A.,The pricing of options on assets with stochastic volatilities. Journal of Finance,1987,(42):281-300.
[18] Chesney M, Scott,L.O. Pricing european options: A comparison of the modifiedblack-scholes model and a random variance model. Financial Quant.Anal., 1989,(24):267-284.
[19] Andersen,T.G. and Bollerslev T. Deutschemark-Dollar volatility: Intraday activity patterns,Macroeconomic announcements, and Longer run dependencies. Journal of Finance,1998,(53):219-265
[20] Anderson,T.G., Bollerslev T and Lange S. Forecasting financial market volatility:Sample frequency vis-à-vis forecast horizon. Journal of Empirical Finance,1999,(6):457-477
[21] Anderson,T.G., Bollerslev T, Diebold,F.X., et al. Great realizations.Risk,2000 ,(13):105-108
[22] Andersen,T.G., Bollerslev T, Diebold,F.X., et al. H.The distribution of realized stock return volatility. Journal of Financial Economies, 2001,(61):43-76
[23] Andersen,T.G., Bollerslev T and Meddahi N. Analytic Evaluation of Volatility Forecasts. International Economic Review, 2004,(45): 1079-1110.
[24] Yeh,Y.H. and T.S.Lee. The Interaction and Volatility Asymmetry of Unexpected Return in the Greater China Stock Markets. Global Finance Journal,2001,(11):129-149.
[25]陈彬.我国证券市场收益波动度及相关性分析.现代财经-天津财经学院学报,2001,(11).
[26]吴雄伟,谢赤.银行间债券市场回购利率的ARCH/GARCH模型及其波动性分析.系统工程,2002,(20):88-91
[27]李亚静,何跃,朱宏泉.中国股市收益率与波动性长记忆性的实证研究.系统工程理论与实践,2003,(23):9-15
[28]陈守东,陈雷,刘艳武.中国沪深股市收益率及波动性相关分析.金融研究,2003,(7):80-85
[29]赵留彦,王一鸣.沪深股市交易量与收益率及其波动的相关性:来自实证分析的证据.经济科学,2003,(2):57-67
[30]刘裕荷,徐伟,张凌梅,于慧君.银行间债券市场回购利率波动性分析.郑州大学学报,2006,(3).
[31]林娟,杨凌.银行间债券市场7天回购利率波动性分析.福州大学学报,2007,(2):45-47
[32]潘婉彬,陶利斌,缪柏其.中国银行间拆借利率扩散模型的极大拟似然估计.数理统计与管理,2007,26(1):158-163
[33]孙明娟,乔克林,孔春香.双因子SV利率波动模型的Bayes估计.江西科学,2009,27(3):345-347
[34]任兆璋,彭化非.我国同业拆借利率期限结构研究.金融研究,2005,3(297):28-37
[35]刘金全,郑挺国.利率期限结构的马尔科夫区制转移模型与实证分析.经济研究,2006,(11):82-91.
[36]张娜,黄新飞,刘登.我国同业拆借市场利率的波动性.财经论坛,2006,(4):121-123
[37]许友传,何佳,杨继光.基于交易头寸的银行市场风险测度方法—以银行间同业拆借市场为例.金融研究,2007,(7):36-46
[38]韦鲁鹏.我国金融市场基础资产波动率与衍生产品创新及监管研究:[对外经济贸易大学博士学位论文].北京:2007
[39]谢玲芳. Shibor报价偏离度对存款准备金上调预示性研究.中国货币市场,2007,(8):46-48
[40]谭晓波,曾远辉.上海银行间同业拆放利率波动率特性研究.华商,2008,(7):12-13.
[41]曹志鹏,韩保林.中国银行间同业拆借市场利率波动模型研究.统计与信息论坛,2008,(12):.59-63
[42]陈浪南,童汉飞,洪如明等.波动率研究.北京:中国财政经济出版社,2008(8).
[43]刘莎莎.基于日历效应和厚尾分布的收益率波动性分析.河北大学硕士学位论文.河北:2010,7-17
[44]常松,何建敏.我国股票市场的非线性性态研究.数量经济技术经济研究,2000,(11):193-195.
[45]郭冰.我国证券投资收益率波动性特征的实证研究.:[同济大学硕士学位论文].上海:2005
[46]胡小芳.对Shibor运行效应的实证分析及未来展望.理论研讨,2008,(3):94-95
[47]黄霖.利率波动的传递及中美比较研究:[南京理工大学硕士学位论文].江苏:2009
[48]李成,马国校. VaR模型在中国银行同业拆借市场中的应用.金融研究,2007,(5):62-76
[49]李海涛,王欣,方兆本.基于偏t分布的Shibor隔夜拆借利率影响因素分析.系统工程,2008,(9).
[50]李群.基于分形市场理论的金融波动性研究:[电子科技大学硕士学位论文].西安:2009,5-10
[51]林小红.金融衍生工具在我国商业银行发展中的作用和潜在风险.新金融,2004,(11).
[52]刘国光,王慧敏.基于纯粹跳跃利维过程的中外股票收益分布特征研究.数理统计与管理,2006,25(1):43-46
[53]门明.金融工程学.北京:对外经济贸易大学出版社,2000.
[54]商勇.中国国债回购利率波动率预测.统计与信息论坛,2007,(9).
[55]谢腾云.基于ARCH模型的沪深股指收益率波动特征分析:[湖南大学硕士学位论文].湖南:2007,13-27
[56]徐铮.中国货币市场短期利率波动的动态加权估计:[中国科学技术大学硕士学位论文].安徽:2010,6-18
[57]杨超,马薇.中国A、B股市场收益波动风险的度量及比较.世界经济,2003,(4).
[58]杨婧.我国股票市场收益率及其波动性的双长期记忆性研究:[浙江大学硕士学位论文].浙江:2009,3-10
[59]易丹辉.数据分析与Eviews应用.北京:中国统计出版社,2002,(10)
[60]袁志湘.我国股市波动非对称性特征的实证研究:[湖南大学硕士学位论文].湖南:2008,27-32
[61]张林,何广文.我国货币市场基准利率SHIBOR实证分析及运行评价.金融理论与实践,2009,(4):9-12
[62]柏宝春,孙松,王晓艺.货币市场基准利率实证研究—基于SHIBOR的分析.金融与经济,2009,(7):23-26
[1] Engle,R.F. Autoregressive Conditional heteroskedasticity with Estimates of the Variance of the United Kingdom Inflation. Econometrica,1982,(50):987-1007.
[2] Taylor S. Modelling Finaneial Time Series.New York:Chichester, John Wiley and Sons,1986
[3] Andersen,T.G. and T.Bollerslev. Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts.International Economic Review,1998,(39):885-905.
[4] Sehwert,W.G. Why does stock market volatility ehange over time, Journal of Finanee,1989,(44):1368-1388
[5] Campbell,J.Y., Grossman,S.J., Wang J. Trading volume and serial correlation in stock returns.Wuarterly Journal of Economies, 1993,(108):905-939
[6] Nakagawa, Shinobu, NaotoOsawa. Financial Market and Macroeconomic Volatility—Relationships and Some Puzzles.Bank of Japan Working Paper,2000,00-9
[7] Mandelbrot B. The variation of some other speculative prices.In:P.Cootner,ed.The Random Character of Stoek Prices. Cambridge, MA:M.I.T.Press,1964
[8] Fama E. The Behavior of Stock Market Prices.Journal of Business,1965,(l),34-105
[9] Bollerslev T. Generalized autoregressive conditional heterskedasticity. Journal of Econometrics,1986,(31):307-327.
[10] Engle,R.F., Lilien,D.M. and Robins,R.P. Estimating time-varying risk premia in the term structure: The ARCH-M model, Econometrica, 1987,(55):391-407
[11] Fischer Black. The Dividend Puzzle.Journal of Portfolio Management, Winter 1976:5-8
[12] Nelson,D.B. ARCH models as diffusion approximations. Journal of Econometrics, 1990,(45):7-38
[13] Zakoian,J.M. Threshold heteroskedastic models.Journal of Economic Dynamics and Control,1990,(18):931-955
[14] Ding,Z., Granger,C.W.J. and Engle,R.F. A long memory property of stoek market returns and a new model.Journal of Empirical Finanee,1993,(l):83-106
[15] Ding,Z. and Granger,C.W.J. Modeling volatility persistence of spceulative returns:A new approach.J.Econometrics.1996,(73):185-215
[16] Engle R,Lee G. A permanent and transitory component model of stoek return volatility.Department of Economics,UCSD,1993
[17] Hull J. and White A.,The pricing of options on assets with stochasticvolatilities.Journal of Finance,1987,(42):281-300.
[18] Chesney M, Scott,L.O. Pricing european options: A comparison of the modified black-scholes model and a random variance model. J.Financial Quant.Anal., 1989,(24):267-284.
[19] Andersen,T.G. and Bollerslev T. Deutschemark-Dollar volatility: Intraday activity patterns,Macroeconomic announcements, and Longer run dependencies. Journal of Finance,1998,(53):219-265
[20] Anderson,T.G., Bollerslev T and Lange S. Forecasting financial market volatility:Sample frequency vis-à-vis forecast horizon.Journal of Empirical Finance,1999,(6):457-477
[21] Anderson,T.G., Bollerslev T, Diebold,F.X., et al. Great realizations.Risk,2000,(13):105-108
[22] Andersen,T.G., Bollerslev T, Diebold,F.X., et al. H.The distribution of realized stock return volatility.Journal of Financial Economies, 2001,(61):43-76
[23] Andersen,T.G., Bollerslev T and Meddahi N. Analytic Evaluation of Volatility Forecasts. International Economic Review, 2004,(45): 1079-1110.
[24] Yeh,Y.H. and T.S.Lee. The Interaction and Volatility Asymmetry of Unexpected Return in the Greater China Stock Markets. Global Finance Journal,2001,(11):129-149.
[25]陈彬.我国证券市场收益波动度及相关性分析.现代财经-天津财经学院学,2001,(11).
[26]吴雄伟,谢赤.银行间债券市场回购利率的ARCH/GARCH模型及其波动性分析.系统工程,2002,(20):88-91
[27]李亚静,何跃,朱宏泉.中国股市收益率与波动性长记忆性的实证研究.系统工程理论与实践,2003,(23):9-15
[28]陈守东,陈雷,刘艳武.中国沪深股市收益率及波动性相关分析.金融研究,2003,(7):80-85
[29]赵留彦,王一鸣.沪深股市交易量与收益率及其波动的相关性:来自实证分析的证据.经济科学,2003,(2):57-67
[30]刘裕荷,徐伟,张凌梅,于慧君.银行间债券市场回购利率波动性分析.郑州大学学报,2006,(3).
[31]林娟,杨凌.银行间债券市场7天回购利率波动性分析.福州大学学报,2007,(2):45-47
[32]潘婉彬,陶利斌,缪柏其.中国银行间拆借利率扩散模型的极大拟似然估计.数理统计与管理,2007,26(1):158-163
[33]孙明娟,乔克林,孔春香.双因子SV利率波动模型的Bayes估计.江西科学,2009,27(3):345-347
[34]任兆璋,彭化非.我国同业拆借利率期限结构研究.金融研究,2005,3(297):28-37
[35]刘金全,郑挺国.利率期限结构的马尔科夫区制转移模型与实证分析.经济研究,2006,(11):82-91.
[36]张娜,黄新飞,刘登.我国同业拆借市场利率的波动性.财经论坛,2006,(4):121-123
[37]许友传,何佳,杨继光.基于交易头寸的银行市场风险测度方法—以银行间同业拆借市场为例.金融研究,2007,(7):36-46
[38]韦鲁鹏.我国金融市场基础资产波动率与衍生产品创新及监管研究:[对外经济贸易大学博士学位论文].北京:2007
[39]谢玲芳. Shibor报价偏离度对存款准备金上调预示性研究.中国货币市场,2007,(8):46-48
[40]谭晓波,曾远辉.上海银行间同业拆放利率波动率特性研.华商,2008,(7):12-13.
[41]曹志鹏,韩保林.中国银行间同业拆借市场利率波动模型研究.统计与信息论坛,2008,(12):.59-63
[41]陈浪南,童汉飞,洪如明等.波动率研究.北京:中国财政经济出版社,2008(8).
[42]刘莎莎.基于日历效应和厚尾分布的收益率波动性分析:[河北大学硕士学位论文].河北:2010,7-17
[43]常松,何建敏.我国股票市场的非线性性态研究.数量经济技术经济研究,2000,(11):193-195.
[44]郭冰.我国证券投资收益率波动性特征的实证研究:[同济大学硕士学位论文].上海:2005,
[45]胡小芳.对Shibor运行效应的实证分析及未来展望.理论研讨,2008 (3):94-95
[46]黄霖.利率波动的传递及中美比较研究:[南京理工大学硕士学位论文].江苏:2009,
[47]李成,马国校. VaR模型在中国银行同业拆借市场中的应用.金融研究,2007(5):62-76
[48]李海涛,王欣,方兆本.基于偏t分布的Shibor隔夜拆借利率影响因素分析.系统工程,2008(9).
[49]李群.基于分形市场理论的金融波动性研究:[电子科技大学硕士学位论文].:2009,5-10
[50]林小红.金融衍生工具在我国商业银行发展中的作用和潜在风险.新金融,2004,(11).
[51]刘国光,王慧敏.基于纯粹跳跃利维过程的中外股票收益分布特征研究.数理统计与管理,2006,25(1):43-46
[52]门明.金融工程学.北京:对外经济贸易大学出版社,2000.
[53]商勇.中国国债回购利率波动率预测.统计与信息论坛,2007(9).
[54]谢腾云.基于ARCH模型的沪深股指收益率波动特征分析:[湖南大学硕士学位论文].湖南:2007,13-27
[55]徐铮.中国货币市场短期利率波动的动态加权估计:[中国科学技术大学硕士学位论文].安徽:2010,6-18
[56]杨超,马薇.中国A、B股市场收益波动风险的度量及比较.世界经济,2003(4).
[58]杨婧.我国股票市场收益率及其波动性的双长期记忆性研究:[浙江大学硕士学位论文].浙江:2009,3-10
[59]易丹辉.数据分析与Eviews应用.北京:中国统计出版社,2002(10)
[60]袁志湘.我国股市波动非对称性特征的实证研究:[湖南大学硕士学位论文].湖南:2008,27-32
[61]张林,何广文.我国货币市场基准利率SHIBOR实证分析及运行评价.金融理论与实践,2009(4):9-12
[62]柏宝春,孙松,王晓艺.货币市场基准利率实证研究——基于SHIBOR的分析.金融与经济,2009(7):23-26.
[2] Taylor S. Modelling Finaneial Time Series. New York:Chichester, John Wiley and Sons,1986
[3] Andersen,T.G. and T.Bollerslev. Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts. International Economic Review,1998,(39):885-905.
[4] Sehwert,W.G. Why does stock market volatility change over time. Journal of Finanee,1989,(44):1368-1388
[5] Campbell,J.Y., Grossman,S.J., Wang J. Trading volume and serial correlation in stock returns. Wuarterly Journal of Economies, 1993,(108):905-939
[6] Nakagawa, Shinobu, NaotoOsawa. Financial Market and Macroeconomic Volatility- -Relationships and Some Puzzles. Bank of Japan Working Paper,2000,00-9
[7] Mandelbrot B. The variation of some other speculative prices.In:P.Cootner,ed.The Random Character of Stoek Prices. Cambridge, MA:M.I.T.Press,1964
[8] Fama E. The Behavior of Stock Market Prices. Journal of Business,1965,(l),34-105
[9] Bollerslev T. Generalized autoregressive conditional heterskedasticity. Journal of Econometrics,1986,(31):307-327.
[10] Engle,R.F., Lilien,D.M. and Robins,R.P. Estimating time-varying risk premia in the term structure: The ARCH-M model. Econometrica, 1987,(55):391-407
[11] Fischer Black. The Dividend Puzzle. Journal of Portfolio Management, Winter 1976:5-8
[12] Nelson,D.B. ARCH models as diffusion approximations. Journal of Econometrics, 1990,(45):7-38
[13] Zakoian,J.M. Threshold heteroskedastic models. Journal of Economic Dynamics and Control,1990,(18):931-955
[14] Ding,Z., Granger,C.W.J. and Engle,R.F. A long memory property of stoek market returns and a new model. Journal of Empirical Finanee,1993,(l):83-106
[15] Ding,Z. and Granger,C.W.J. Modeling volatility persistence of spceulative returns:A new approach. Econometrics.1996,(73):185-215
[16] Engle R,Lee G. A permanent and transitory component model of stoek return volatility. Department of Economics,UCSD,1993
[17] Hull J. and White A.,The pricing of options on assets with stochastic volatilities. Journal of Finance,1987,(42):281-300.
[18] Chesney M, Scott,L.O. Pricing european options: A comparison of the modifiedblack-scholes model and a random variance model. Financial Quant.Anal., 1989,(24):267-284.
[19] Andersen,T.G. and Bollerslev T. Deutschemark-Dollar volatility: Intraday activity patterns,Macroeconomic announcements, and Longer run dependencies. Journal of Finance,1998,(53):219-265
[20] Anderson,T.G., Bollerslev T and Lange S. Forecasting financial market volatility:Sample frequency vis-à-vis forecast horizon. Journal of Empirical Finance,1999,(6):457-477
[21] Anderson,T.G., Bollerslev T, Diebold,F.X., et al. Great realizations.Risk,2000 ,(13):105-108
[22] Andersen,T.G., Bollerslev T, Diebold,F.X., et al. H.The distribution of realized stock return volatility. Journal of Financial Economies, 2001,(61):43-76
[23] Andersen,T.G., Bollerslev T and Meddahi N. Analytic Evaluation of Volatility Forecasts. International Economic Review, 2004,(45): 1079-1110.
[24] Yeh,Y.H. and T.S.Lee. The Interaction and Volatility Asymmetry of Unexpected Return in the Greater China Stock Markets. Global Finance Journal,2001,(11):129-149.
[25]陈彬.我国证券市场收益波动度及相关性分析.现代财经-天津财经学院学报,2001,(11).
[26]吴雄伟,谢赤.银行间债券市场回购利率的ARCH/GARCH模型及其波动性分析.系统工程,2002,(20):88-91
[27]李亚静,何跃,朱宏泉.中国股市收益率与波动性长记忆性的实证研究.系统工程理论与实践,2003,(23):9-15
[28]陈守东,陈雷,刘艳武.中国沪深股市收益率及波动性相关分析.金融研究,2003,(7):80-85
[29]赵留彦,王一鸣.沪深股市交易量与收益率及其波动的相关性:来自实证分析的证据.经济科学,2003,(2):57-67
[30]刘裕荷,徐伟,张凌梅,于慧君.银行间债券市场回购利率波动性分析.郑州大学学报,2006,(3).
[31]林娟,杨凌.银行间债券市场7天回购利率波动性分析.福州大学学报,2007,(2):45-47
[32]潘婉彬,陶利斌,缪柏其.中国银行间拆借利率扩散模型的极大拟似然估计.数理统计与管理,2007,26(1):158-163
[33]孙明娟,乔克林,孔春香.双因子SV利率波动模型的Bayes估计.江西科学,2009,27(3):345-347
[34]任兆璋,彭化非.我国同业拆借利率期限结构研究.金融研究,2005,3(297):28-37
[35]刘金全,郑挺国.利率期限结构的马尔科夫区制转移模型与实证分析.经济研究,2006,(11):82-91.
[36]张娜,黄新飞,刘登.我国同业拆借市场利率的波动性.财经论坛,2006,(4):121-123
[37]许友传,何佳,杨继光.基于交易头寸的银行市场风险测度方法—以银行间同业拆借市场为例.金融研究,2007,(7):36-46
[38]韦鲁鹏.我国金融市场基础资产波动率与衍生产品创新及监管研究:[对外经济贸易大学博士学位论文].北京:2007
[39]谢玲芳. Shibor报价偏离度对存款准备金上调预示性研究.中国货币市场,2007,(8):46-48
[40]谭晓波,曾远辉.上海银行间同业拆放利率波动率特性研究.华商,2008,(7):12-13.
[41]曹志鹏,韩保林.中国银行间同业拆借市场利率波动模型研究.统计与信息论坛,2008,(12):.59-63
[42]陈浪南,童汉飞,洪如明等.波动率研究.北京:中国财政经济出版社,2008(8).
[43]刘莎莎.基于日历效应和厚尾分布的收益率波动性分析.河北大学硕士学位论文.河北:2010,7-17
[44]常松,何建敏.我国股票市场的非线性性态研究.数量经济技术经济研究,2000,(11):193-195.
[45]郭冰.我国证券投资收益率波动性特征的实证研究.:[同济大学硕士学位论文].上海:2005
[46]胡小芳.对Shibor运行效应的实证分析及未来展望.理论研讨,2008,(3):94-95
[47]黄霖.利率波动的传递及中美比较研究:[南京理工大学硕士学位论文].江苏:2009
[48]李成,马国校. VaR模型在中国银行同业拆借市场中的应用.金融研究,2007,(5):62-76
[49]李海涛,王欣,方兆本.基于偏t分布的Shibor隔夜拆借利率影响因素分析.系统工程,2008,(9).
[50]李群.基于分形市场理论的金融波动性研究:[电子科技大学硕士学位论文].西安:2009,5-10
[51]林小红.金融衍生工具在我国商业银行发展中的作用和潜在风险.新金融,2004,(11).
[52]刘国光,王慧敏.基于纯粹跳跃利维过程的中外股票收益分布特征研究.数理统计与管理,2006,25(1):43-46
[53]门明.金融工程学.北京:对外经济贸易大学出版社,2000.
[54]商勇.中国国债回购利率波动率预测.统计与信息论坛,2007,(9).
[55]谢腾云.基于ARCH模型的沪深股指收益率波动特征分析:[湖南大学硕士学位论文].湖南:2007,13-27
[56]徐铮.中国货币市场短期利率波动的动态加权估计:[中国科学技术大学硕士学位论文].安徽:2010,6-18
[57]杨超,马薇.中国A、B股市场收益波动风险的度量及比较.世界经济,2003,(4).
[58]杨婧.我国股票市场收益率及其波动性的双长期记忆性研究:[浙江大学硕士学位论文].浙江:2009,3-10
[59]易丹辉.数据分析与Eviews应用.北京:中国统计出版社,2002,(10)
[60]袁志湘.我国股市波动非对称性特征的实证研究:[湖南大学硕士学位论文].湖南:2008,27-32
[61]张林,何广文.我国货币市场基准利率SHIBOR实证分析及运行评价.金融理论与实践,2009,(4):9-12
[62]柏宝春,孙松,王晓艺.货币市场基准利率实证研究—基于SHIBOR的分析.金融与经济,2009,(7):23-26
[1] Engle,R.F. Autoregressive Conditional heteroskedasticity with Estimates of the Variance of the United Kingdom Inflation. Econometrica,1982,(50):987-1007.
[2] Taylor S. Modelling Finaneial Time Series.New York:Chichester, John Wiley and Sons,1986
[3] Andersen,T.G. and T.Bollerslev. Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts.International Economic Review,1998,(39):885-905.
[4] Sehwert,W.G. Why does stock market volatility ehange over time, Journal of Finanee,1989,(44):1368-1388
[5] Campbell,J.Y., Grossman,S.J., Wang J. Trading volume and serial correlation in stock returns.Wuarterly Journal of Economies, 1993,(108):905-939
[6] Nakagawa, Shinobu, NaotoOsawa. Financial Market and Macroeconomic Volatility—Relationships and Some Puzzles.Bank of Japan Working Paper,2000,00-9
[7] Mandelbrot B. The variation of some other speculative prices.In:P.Cootner,ed.The Random Character of Stoek Prices. Cambridge, MA:M.I.T.Press,1964
[8] Fama E. The Behavior of Stock Market Prices.Journal of Business,1965,(l),34-105
[9] Bollerslev T. Generalized autoregressive conditional heterskedasticity. Journal of Econometrics,1986,(31):307-327.
[10] Engle,R.F., Lilien,D.M. and Robins,R.P. Estimating time-varying risk premia in the term structure: The ARCH-M model, Econometrica, 1987,(55):391-407
[11] Fischer Black. The Dividend Puzzle.Journal of Portfolio Management, Winter 1976:5-8
[12] Nelson,D.B. ARCH models as diffusion approximations. Journal of Econometrics, 1990,(45):7-38
[13] Zakoian,J.M. Threshold heteroskedastic models.Journal of Economic Dynamics and Control,1990,(18):931-955
[14] Ding,Z., Granger,C.W.J. and Engle,R.F. A long memory property of stoek market returns and a new model.Journal of Empirical Finanee,1993,(l):83-106
[15] Ding,Z. and Granger,C.W.J. Modeling volatility persistence of spceulative returns:A new approach.J.Econometrics.1996,(73):185-215
[16] Engle R,Lee G. A permanent and transitory component model of stoek return volatility.Department of Economics,UCSD,1993
[17] Hull J. and White A.,The pricing of options on assets with stochasticvolatilities.Journal of Finance,1987,(42):281-300.
[18] Chesney M, Scott,L.O. Pricing european options: A comparison of the modified black-scholes model and a random variance model. J.Financial Quant.Anal., 1989,(24):267-284.
[19] Andersen,T.G. and Bollerslev T. Deutschemark-Dollar volatility: Intraday activity patterns,Macroeconomic announcements, and Longer run dependencies. Journal of Finance,1998,(53):219-265
[20] Anderson,T.G., Bollerslev T and Lange S. Forecasting financial market volatility:Sample frequency vis-à-vis forecast horizon.Journal of Empirical Finance,1999,(6):457-477
[21] Anderson,T.G., Bollerslev T, Diebold,F.X., et al. Great realizations.Risk,2000,(13):105-108
[22] Andersen,T.G., Bollerslev T, Diebold,F.X., et al. H.The distribution of realized stock return volatility.Journal of Financial Economies, 2001,(61):43-76
[23] Andersen,T.G., Bollerslev T and Meddahi N. Analytic Evaluation of Volatility Forecasts. International Economic Review, 2004,(45): 1079-1110.
[24] Yeh,Y.H. and T.S.Lee. The Interaction and Volatility Asymmetry of Unexpected Return in the Greater China Stock Markets. Global Finance Journal,2001,(11):129-149.
[25]陈彬.我国证券市场收益波动度及相关性分析.现代财经-天津财经学院学,2001,(11).
[26]吴雄伟,谢赤.银行间债券市场回购利率的ARCH/GARCH模型及其波动性分析.系统工程,2002,(20):88-91
[27]李亚静,何跃,朱宏泉.中国股市收益率与波动性长记忆性的实证研究.系统工程理论与实践,2003,(23):9-15
[28]陈守东,陈雷,刘艳武.中国沪深股市收益率及波动性相关分析.金融研究,2003,(7):80-85
[29]赵留彦,王一鸣.沪深股市交易量与收益率及其波动的相关性:来自实证分析的证据.经济科学,2003,(2):57-67
[30]刘裕荷,徐伟,张凌梅,于慧君.银行间债券市场回购利率波动性分析.郑州大学学报,2006,(3).
[31]林娟,杨凌.银行间债券市场7天回购利率波动性分析.福州大学学报,2007,(2):45-47
[32]潘婉彬,陶利斌,缪柏其.中国银行间拆借利率扩散模型的极大拟似然估计.数理统计与管理,2007,26(1):158-163
[33]孙明娟,乔克林,孔春香.双因子SV利率波动模型的Bayes估计.江西科学,2009,27(3):345-347
[34]任兆璋,彭化非.我国同业拆借利率期限结构研究.金融研究,2005,3(297):28-37
[35]刘金全,郑挺国.利率期限结构的马尔科夫区制转移模型与实证分析.经济研究,2006,(11):82-91.
[36]张娜,黄新飞,刘登.我国同业拆借市场利率的波动性.财经论坛,2006,(4):121-123
[37]许友传,何佳,杨继光.基于交易头寸的银行市场风险测度方法—以银行间同业拆借市场为例.金融研究,2007,(7):36-46
[38]韦鲁鹏.我国金融市场基础资产波动率与衍生产品创新及监管研究:[对外经济贸易大学博士学位论文].北京:2007
[39]谢玲芳. Shibor报价偏离度对存款准备金上调预示性研究.中国货币市场,2007,(8):46-48
[40]谭晓波,曾远辉.上海银行间同业拆放利率波动率特性研.华商,2008,(7):12-13.
[41]曹志鹏,韩保林.中国银行间同业拆借市场利率波动模型研究.统计与信息论坛,2008,(12):.59-63
[41]陈浪南,童汉飞,洪如明等.波动率研究.北京:中国财政经济出版社,2008(8).
[42]刘莎莎.基于日历效应和厚尾分布的收益率波动性分析:[河北大学硕士学位论文].河北:2010,7-17
[43]常松,何建敏.我国股票市场的非线性性态研究.数量经济技术经济研究,2000,(11):193-195.
[44]郭冰.我国证券投资收益率波动性特征的实证研究:[同济大学硕士学位论文].上海:2005,
[45]胡小芳.对Shibor运行效应的实证分析及未来展望.理论研讨,2008 (3):94-95
[46]黄霖.利率波动的传递及中美比较研究:[南京理工大学硕士学位论文].江苏:2009,
[47]李成,马国校. VaR模型在中国银行同业拆借市场中的应用.金融研究,2007(5):62-76
[48]李海涛,王欣,方兆本.基于偏t分布的Shibor隔夜拆借利率影响因素分析.系统工程,2008(9).
[49]李群.基于分形市场理论的金融波动性研究:[电子科技大学硕士学位论文].:2009,5-10
[50]林小红.金融衍生工具在我国商业银行发展中的作用和潜在风险.新金融,2004,(11).
[51]刘国光,王慧敏.基于纯粹跳跃利维过程的中外股票收益分布特征研究.数理统计与管理,2006,25(1):43-46
[52]门明.金融工程学.北京:对外经济贸易大学出版社,2000.
[53]商勇.中国国债回购利率波动率预测.统计与信息论坛,2007(9).
[54]谢腾云.基于ARCH模型的沪深股指收益率波动特征分析:[湖南大学硕士学位论文].湖南:2007,13-27
[55]徐铮.中国货币市场短期利率波动的动态加权估计:[中国科学技术大学硕士学位论文].安徽:2010,6-18
[56]杨超,马薇.中国A、B股市场收益波动风险的度量及比较.世界经济,2003(4).
[58]杨婧.我国股票市场收益率及其波动性的双长期记忆性研究:[浙江大学硕士学位论文].浙江:2009,3-10
[59]易丹辉.数据分析与Eviews应用.北京:中国统计出版社,2002(10)
[60]袁志湘.我国股市波动非对称性特征的实证研究:[湖南大学硕士学位论文].湖南:2008,27-32
[61]张林,何广文.我国货币市场基准利率SHIBOR实证分析及运行评价.金融理论与实践,2009(4):9-12
[62]柏宝春,孙松,王晓艺.货币市场基准利率实证研究——基于SHIBOR的分析.金融与经济,2009(7):23-26.