应用变系数分位点回归模型分析经济因素对全球股市风险的影响
|
摘要
全球股市与经济因素之间关系一直是金融领域的热点问题,当前已有研究大都基于线性回归模型分析经济因素对股市收益率均值的影响。本文则基于变系数分位点回归模型研究了全球主要股票市场指数的市场风险与影响因素之间随时间变化的相依关系,对英国、法国、德国、加拿大、巴西、日本以及中国等主要国家股市1997年10月到2014年12月的数据进行了实证研究。实证研究结果表明,S&P500指数、商品市场(石油价格,黄金价格)等经济因素对全球主要股市的风险(下分位点)产生较为显著的影响,并且上述影响随着近期金融危机的发生而有所变化。相比之下,美国经济政策的不确定性、美国股市不确定性变化等对所分析国家股票市场风险的影响不显著。
The analysis of the dependence structure between the stock markets of the major country of the world and influential global factors has always been a hot topic in international finance area. The literatures before focused on the impact of economic factors on the mean of stock returns by using the linear regression model. This paper examines the dependence structure between the stock markets of the major country of the world and influential global factors by using the quantile regression with varying coefficients approach. Our study base on the stock returns of Britain, France, Germany, Canada, Brazil,Japan and China for the period from October 1997 to December 2014. The results show that the stock markets risk(lower quantile) exhibit dependence with the global stock(S&P500 index) as well as the commodity markets(oil, gold). This dependence structure is affected by the onset of the recent global financial crisis. By contrast, the impact of the changes in the U.S. stock market uncertainty(CBOE Volatility Index)and the U.S. economic policyuncertainty is almost not significant.
The analysis of the dependence structure between the stock markets of the major country of the world and influential global factors has always been a hot topic in international finance area. The literatures before focused on the impact of economic factors on the mean of stock returns by using the linear regression model. This paper examines the dependence structure between the stock markets of the major country of the world and influential global factors by using the quantile regression with varying coefficients approach. Our study base on the stock returns of Britain, France, Germany, Canada, Brazil,Japan and China for the period from October 1997 to December 2014. The results show that the stock markets risk(lower quantile) exhibit dependence with the global stock(S&P500 index) as well as the commodity markets(oil, gold). This dependence structure is affected by the onset of the recent global financial crisis. By contrast, the impact of the changes in the U.S. stock market uncertainty(CBOE Volatility Index)and the U.S. economic policyuncertainty is almost not significant.
引文
[1] Fama E F, French K R. Business conditions and expected returns on stocks and bonds[J]. Journal of Financial Economics, 1989, 25(1):23-49.
[2] Kenourgios D, Samitas A, Paltalidis N. Financial crises and stock market contagion in a multivariate time-varying asymmetric framework[J]. Journal of International Financial Markets, Institutions and Money, 2011, 21(1):92-106.
[3] Mensi W, Hammoudeh S, Reboredo J C, et al. Do global factors impact BRICS stock markets? A quantile regression approach[J]. Emerging Markets Review, 2014, 19:1-17.
[4] Kang W, Ratti R A. Oil shocks, policy uncertainty and stock market return[J]. Journal of International Financial Markets, Institutions and Money, 2013, 26:305-318.
[5] Dimitriou D, Kenourgios D, Simos T. Global financial crisis and emerging stock market contagion:A multivariate FIAPARCH-DCC approach[J]. International Review of Financial Analysis, 2013, 30:46-56.
[6] Koenker R, Bassett Jr G. Regression quantiles[J]. Econometrica:Journal of the Econometric Society,1978:33-50.
[7] Engle R F, Manganelli S. CAViaR:Conditional autoregressive value at risk by regression quantiles[J]. Journal of Business&Economic Statistics, 2004, 22(4):367-381.
[8] Park B J. On the quantile regression based tests for asymmetry in stock return volatility[J]. Asian Economic Journal, 2002, 16(2):175-191.
[9] Taylor J W. A quantile regression approach to estimating the distribution of multiperiod returns[J].The Journal of Derivatives, 1999, 7(1):64-78.
[10] Chernozhukov V, Umantsev L. Conditional value-at-risk:Aspects of modeling and estimation[J].Empirical Economics, 2001, 26(1):271-292.
[11] Baur D G. The structure and degree of dependence:A quantile regression approach[J]. Journal of Banking&Finance, 2013, 37(3):786-798.
[12] Chuang C C, Kuan C M, Lin H. Causality in quantiles and dynamic stock ret urn-volume relations[J]. Journal of Banking&Finance, 2009, 33(7):1351-1360.
[13]郑承利,陈灯塔.中国股市截面收益率再研究:分位数回归方法[J].南方经济,2006, 1:61-71.
[14]叶五一,缪柏其,谭常春.基于分位点回归模型变点检测的金融传染分析[J].数量经济技术经济研究,2007, 24(10):151-160.
[15]史金凤,刘维奇,杨威.基于分位数回归的金融市场稳定性检验[J].中国管理科学,2011,19(2):24-29.
[16]李劭珉,任燕燕.一种面板数据分位数回归模型的工具变量方法[J].数理统计与管理,2017, 36(5):943-950.
[17]马绰欣,田茂再.基于面板分位回归方法的我国金融发展对城乡收入差距影响分析[J].数理统计与管理,2017, 36(2):341-350.
[18] Hong Y, Lee T H. Inference on predictability of foreign exchange rates via generalized spectrum and nonlinear time series models[J]. Review of Economics and Statistics, 2003, 85(4):1048-1062.
[19] Cai Z, Fan J, Li R. Efficient estimation and inferences for varying-coefficient models[J]. Journal of the American Statistical Association, 2000, 95(451):888-902.
[20]鲁万波,李竹渝.股票收益率波动性的非参数核回归估计及对中国股市的实证分析[J].高校应用数学学报(A辑),2005, 20(1):9-16.
[21]陈娟.非参数方法在沪深股市收益率分布的应用[J].温州大学学报(社会科学版),2005, 18(3):22-27.
[22] Cai Z, Xu X. Nonparametric quantile estimations for dynamic smooth coefficient models[J]. Journal of the American Statistical Association, 2008, 103(484):371-383.
[23] Honda T. Quantile regression in varying coefficient models[J]. Journal of Statistical Planning and Inference, 2004, 121(1):113-125.
[24] Kim M O. Quantile regression with varying coefficients[J]. The Annals of Statistics, 2007, 35(1):92-108.
[25] Koenker R. Quantile Regression[M]. Cambridge University Press, 2005.
[26]范剑青,姚琦伟著(陈敏译).非线性时间序列-建模、预报及应用[M].北京:高等教育出版社,2011.
[27] Rodrik D. Policy uncertainty and private investment in developing countries[J]. Journal of Development Economics, 1991, 36(2):229-242.
[28] Brogaard J, Detzel A. The Asset-pricing implications of government economic policy uncertainty[J].Management Science, 2015, 61(1):3-18.
[29] Baker S R, Bloom N, Davis S J. Measuring economic policy uncertainty[R]. Chicago Booth research paper, 2013(13-02).
[30] Phillips P C B, Perron P. Testing for a unit root in time series regression[J]. Biometrika, 1988, 75(2):335-346.
[31] Kwiatkowski D, Phillips P C B, Schmidt P, et al. Testing the null hypothesis of stationarity against the alternative of a unit root:How sure are we that economic time series have a unit root?[J].Journal of econometrics, 1992, 54(1):159-178.
[32]叶五一,缪柏其.基于Copula变点检测的美国次级债金融危机传染分析[J].中国管理科学,2009, 17(3):1-7.
[2] Kenourgios D, Samitas A, Paltalidis N. Financial crises and stock market contagion in a multivariate time-varying asymmetric framework[J]. Journal of International Financial Markets, Institutions and Money, 2011, 21(1):92-106.
[3] Mensi W, Hammoudeh S, Reboredo J C, et al. Do global factors impact BRICS stock markets? A quantile regression approach[J]. Emerging Markets Review, 2014, 19:1-17.
[4] Kang W, Ratti R A. Oil shocks, policy uncertainty and stock market return[J]. Journal of International Financial Markets, Institutions and Money, 2013, 26:305-318.
[5] Dimitriou D, Kenourgios D, Simos T. Global financial crisis and emerging stock market contagion:A multivariate FIAPARCH-DCC approach[J]. International Review of Financial Analysis, 2013, 30:46-56.
[6] Koenker R, Bassett Jr G. Regression quantiles[J]. Econometrica:Journal of the Econometric Society,1978:33-50.
[7] Engle R F, Manganelli S. CAViaR:Conditional autoregressive value at risk by regression quantiles[J]. Journal of Business&Economic Statistics, 2004, 22(4):367-381.
[8] Park B J. On the quantile regression based tests for asymmetry in stock return volatility[J]. Asian Economic Journal, 2002, 16(2):175-191.
[9] Taylor J W. A quantile regression approach to estimating the distribution of multiperiod returns[J].The Journal of Derivatives, 1999, 7(1):64-78.
[10] Chernozhukov V, Umantsev L. Conditional value-at-risk:Aspects of modeling and estimation[J].Empirical Economics, 2001, 26(1):271-292.
[11] Baur D G. The structure and degree of dependence:A quantile regression approach[J]. Journal of Banking&Finance, 2013, 37(3):786-798.
[12] Chuang C C, Kuan C M, Lin H. Causality in quantiles and dynamic stock ret urn-volume relations[J]. Journal of Banking&Finance, 2009, 33(7):1351-1360.
[13]郑承利,陈灯塔.中国股市截面收益率再研究:分位数回归方法[J].南方经济,2006, 1:61-71.
[14]叶五一,缪柏其,谭常春.基于分位点回归模型变点检测的金融传染分析[J].数量经济技术经济研究,2007, 24(10):151-160.
[15]史金凤,刘维奇,杨威.基于分位数回归的金融市场稳定性检验[J].中国管理科学,2011,19(2):24-29.
[16]李劭珉,任燕燕.一种面板数据分位数回归模型的工具变量方法[J].数理统计与管理,2017, 36(5):943-950.
[17]马绰欣,田茂再.基于面板分位回归方法的我国金融发展对城乡收入差距影响分析[J].数理统计与管理,2017, 36(2):341-350.
[18] Hong Y, Lee T H. Inference on predictability of foreign exchange rates via generalized spectrum and nonlinear time series models[J]. Review of Economics and Statistics, 2003, 85(4):1048-1062.
[19] Cai Z, Fan J, Li R. Efficient estimation and inferences for varying-coefficient models[J]. Journal of the American Statistical Association, 2000, 95(451):888-902.
[20]鲁万波,李竹渝.股票收益率波动性的非参数核回归估计及对中国股市的实证分析[J].高校应用数学学报(A辑),2005, 20(1):9-16.
[21]陈娟.非参数方法在沪深股市收益率分布的应用[J].温州大学学报(社会科学版),2005, 18(3):22-27.
[22] Cai Z, Xu X. Nonparametric quantile estimations for dynamic smooth coefficient models[J]. Journal of the American Statistical Association, 2008, 103(484):371-383.
[23] Honda T. Quantile regression in varying coefficient models[J]. Journal of Statistical Planning and Inference, 2004, 121(1):113-125.
[24] Kim M O. Quantile regression with varying coefficients[J]. The Annals of Statistics, 2007, 35(1):92-108.
[25] Koenker R. Quantile Regression[M]. Cambridge University Press, 2005.
[26]范剑青,姚琦伟著(陈敏译).非线性时间序列-建模、预报及应用[M].北京:高等教育出版社,2011.
[27] Rodrik D. Policy uncertainty and private investment in developing countries[J]. Journal of Development Economics, 1991, 36(2):229-242.
[28] Brogaard J, Detzel A. The Asset-pricing implications of government economic policy uncertainty[J].Management Science, 2015, 61(1):3-18.
[29] Baker S R, Bloom N, Davis S J. Measuring economic policy uncertainty[R]. Chicago Booth research paper, 2013(13-02).
[30] Phillips P C B, Perron P. Testing for a unit root in time series regression[J]. Biometrika, 1988, 75(2):335-346.
[31] Kwiatkowski D, Phillips P C B, Schmidt P, et al. Testing the null hypothesis of stationarity against the alternative of a unit root:How sure are we that economic time series have a unit root?[J].Journal of econometrics, 1992, 54(1):159-178.
[32]叶五一,缪柏其.基于Copula变点检测的美国次级债金融危机传染分析[J].中国管理科学,2009, 17(3):1-7.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |