中国债券收益率的可预测性检验
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摘要
基于工具变量(instrumental variable, IVX)统计检验方法,对我国主要宏观经济变量能否单独或联合预测债券收益率进行可预测性研究.IVX检验方法无需考虑预测变量的持续性先验信息,对任何属于单位根过程、近单位根过程、近平稳过程或平稳过程的预测变量都稳健.进一步使用AIC和BIC方法对多元变量回归模型进行变量筛选,仿真结果表明,两种准则均能准确筛选出模型重要变量并排除冗余变量.通过对国债和AAA、AA-级企业债三种不同信用等级债券收益率的长短期可预测性差异分析,研究发现:1) 1年期储蓄存款利率、人民币实际有效汇率、沪深300指数、工业增加值同比以及大部分宏观经济变量组合在10%显著性水平上能显著预测债券收益率;2)宏观经济变量长期预测能力强于短期;3)基于IVX方法的AIC和BIC准则能够有效地筛选模型重要变量.
In this study, we investigate whether bond return can be predicted by macroeconomic variables based on the instrumental variable(IVX) based Wald statistics. The IVX-based testing procedure does not have any prior on the degree of persistence of the predicting variables and robustifies inference regardless of the predicting variable being stationary, or have moderate deviation from a unit root, or instead they are near unit root or unit root process. Meanwhile, AIC and BIC are further introduced into the IVX method to select important variables. Monte Caro simulations suggest that they can select the important variables and exclude redundant variable correctly. By analyzing the differences in the long-term and short-term predictability of the bond yields of three different credit ratings for government bonds, AAA and AA-grade corporate bonds, we find 1) The 1-year savings deposit rate, the real effective RMB exchange rate, the Shanghai and Shenzhen 300 index return, and most of the macroeconomic variables combinations can be used to predict the bond return; 2) Long-term predictability of macroeconomic variables is stronger than the short term; 3) Both AIC and BIC can effectively select important variables.
In this study, we investigate whether bond return can be predicted by macroeconomic variables based on the instrumental variable(IVX) based Wald statistics. The IVX-based testing procedure does not have any prior on the degree of persistence of the predicting variables and robustifies inference regardless of the predicting variable being stationary, or have moderate deviation from a unit root, or instead they are near unit root or unit root process. Meanwhile, AIC and BIC are further introduced into the IVX method to select important variables. Monte Caro simulations suggest that they can select the important variables and exclude redundant variable correctly. By analyzing the differences in the long-term and short-term predictability of the bond yields of three different credit ratings for government bonds, AAA and AA-grade corporate bonds, we find 1) The 1-year savings deposit rate, the real effective RMB exchange rate, the Shanghai and Shenzhen 300 index return, and most of the macroeconomic variables combinations can be used to predict the bond return; 2) Long-term predictability of macroeconomic variables is stronger than the short term; 3) Both AIC and BIC can effectively select important variables.
引文
[1]杨婉茜,成力为.基于贝叶斯向量自回归的中国国债收益率预测[J].统计研究,2015, 32(8):69-76.Yang W X, Cheng L W. Chinese government bond yield forecast based on Bayesian vector autoregression[J].Statistical Research, 2015, 32(8):69-76.
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[4]潘敏,夏庆,刘小燕,等.汇率制度改革、货币政策与国债利率期限结构[J].金融研究,2011(11):18-31.Pan M, Xia Q, Liu X Y, et al. Reform of exchange rate system, monetary policy and term structure of national debt interest rate[J]. Journal of Financial Research, 2011(11):18-31.
[5]刘澜飚,沈鑫,王博.中国宏观经济对国债利率期限结构的影响研究——基于动态随机一般均衡模型的分析[J].金融研究,2014(11):49-63.Liu L B, Shen X, Wang B. Research on the influence of China's macro-economy on the term structure of national debt interest rate—Based on the analysis of dynamic stochastic general equilibrium model[J]. Journal of Financial Research, 2014(11):49-63.
[6]Ang A, Piazzesi M. A no-arbitrage vector autoregression of term structure dynamics with macroeconomic and latent variables[J]. Journal of Monetary Economics, 2003(50):745-787.
[7]Connolly R, Stivers C, Sun L. Stock market uncertainty and the stock-bond return relation[J]. Journal of Financial&Quantitative Analysis, 2005(40):161-194.
[8]Baele L,Bekaert G, Inghelbrecht K. The determinants of stock and bond return comovements[J]. Review of Financial Studies, 2010(23):2374-2428.
[9]Hong Y, Lin H, Wu C. Are corporate bond market returns predictable?[J]. Journal of Banking&Finance,2012(36):2216-2232.
[10]Lin H, Wang J, Wu C. Predictions of corporate bond excess returns[J]. Journal of Financial Market, 2014(21):123-152.
[11]Chen L, Zhang G, Zhang W. Return predictions in the corporate bond market along the supply chain[J]. Journal of Financial Market, 2016(29):66-86.
[12]Phlillips P C B, Moon H. Nonstationary panel data analysis:An overview of some recent developments[J].Econometric Reviews, 2000(19):263-286.
[13]Phillips P C B, Lee J H. Predictive regression under various degrees of persistence and robust long-horizon regression[J]. Journal of Econometrics, 2013(177):250-264.
[14]Welch I, Goyal A. A comprehensive look at the empirical performance of equity premium prediction[J]. Review of Financial Studies, 2008(21):455-508.
[15]Stambaugh R F. Predictive regressions[J]. Journal of Financial Economics, 1999(54):375-421.
[16]Elliott G. On the robustness of cointegration methods when regressors almost have unit roots[J]. Econometrica,1998(66):149-158.
[17]袁裕泽.带有GARCH误差和趋势项的近单位根过程的估计[J].浙江大学学报(理学版),2014, 41(2):145-148.Yuan Y Z. Estimation of near-unit root processes with GARCH errors and trend terms[J]. Journal of Zhejiang University(Science Edition), 2014, 41(2):145-148.
[18]邓伟,唐齐鸣.单位根相关过程:理论的发展与比较[J].经济学动态,2014(2):132-141.Deng W, Tang Q M. Unit root related process:Theoretical development and comparison[J]. Economic Perspectives, 2014(2):132-141.
[19]史永东,何海江,沈德华.中国股市有效性动态变化的实证研究[J].系统工程理论与实践,2002, 22(12):88-92.Shi Y D, He H J, Shen D H. Empirical analysis of efficiency evolution in Chinese stock markets[J]. Systems Engineering—Theory&Practice, 2002, 22(12):88-92.
[20]孟庆芳,张强,牟文英.混沌序列自适应多步预测及在股票中的应用[J].系统工程理论与实践,2005, 25(12):62-68.Meng Q F, Zhang Q, Mu W Y. A novel adaptive multi-step-prediction method for chaotic time series and its applications in stock market[J]. Systems Engineering—Theory&Practice, 2005, 25(12):62-68.
[21]饶育蕾,彭叠峰,贾文静.交叉持股是否导致收益的可预测性?基于有限注意的视角[J].系统工程理论与实践,2013,33(7):1753-1761.Rao Y L, Peng D F, Jia W J. Does cross-shareholding cause return predictability? Based on limited attention perspective[J]. Systems Engineering—Theory&Practice, 2013, 33(7):1753-1761.
[22]陈蓉,赵永杰.隐含波动率曲面的预测研究:来自中国台湾市场的证据[J].系统工程理论与实践,2017, 37(8):1949-1962.Chen R, Zhao Y J. Forecasting implied volatility surface:Evidence from Taiwan China market[J]. Systems Engineering—Theory&Practice, 2017, 37(8):1949-1962.
[23]Phillips P C B, Magdalinos T. Econometric inference in the vicinity of unity[R]. CoFie Working Paper No.7,Singapore Management University, 2009.
[24]Cavanagh C L, Elliott G, Stock J H. Inference in models with nearly integrated regressors[J]. Econometric Theory,1995(11):1131-1147.
[25]Campbell J Y, Yogo M. Efficient tests of stock return predictability[J]. Journal of Financial Economics, 2006(81):27-60.
[26]Cai Z, Wang Y. Testing predictive regression models with nonstationary regressors[J]. Journal of Econometrics,2014(178):4-14.
[27]Chen W W, Deo R S, Yi Y. Uniform inference in predictive regression models[J]. Journal of Business&Economic Statistics, 2013(31):525-533.
[28]Kostakis A, Magdalinos T, Stamatogiannis M P. Robust econometric inference for stock return predictability[J].Review of Financial Studies, 2015(53):1506-1533.
[29]Francq C, Zakoian J M. GARCH models:Structure, statistical inference and financial applications[M]. Hoboken,NJ:Wiley, 2010.
[30]Boudoukh J, Mcallister P, Richardson M P. The valuation and hedging of deferred commission asset backed securities[J]. Social Science Electronic Publishing, 2008. DOI:http://hdl.handle.net/2451/26684.
[31]Ludvigson S C, Ng S. Macro factors in bond risk premia[J]. Review of Financial Studies, 2009(22):5027-5067.
[32]伍戈,曹红钢.中国的结构性通货膨胀研究——基于CPI与PPI的相对变化[J].金融研究,2014(6):1-16.Wu G, Cao H G. China's structural inflation research:Based on relative changes in CPI and PPI[J]. Journal of Financial Research, 2014(6):1-16.
[33]田开兰,孔亦舒,杨翠红.汇率波动对中国出口增加值的影响分析[J].系统工程理论与实践,2017, 37(5):1144-1153.Tian K L, Kong Y S, Yang C H. How much does exchange rate volatility affect China's value-added in exports?[J].Systems Engineering—Theory&Practice, 2017, 37(5):1144-1153.
[34]顾光同,许冰.中国工业增加值的半月预报:基于宏观月度数据[J].系统工程理论与实践,2018, 38(8):1983-1993.Gu G T, Xu B. Half-month forecast of China's industrial added value:Based on the macro monthly data[J].Systems Engineering—Theory&Practice, 2018, 38(8):1983-1993.
[35]杨子晖,赵永亮,柳建华.CPI与PPI传导机制的非线性研究:正向传导还是反向倒逼?[J].经济研究,2013(3):83-95.Yang Z H, Zhao Y L, Liu J H. Nonlinear study of CPI and PPI conduction mechanisms:Forward conduction or reverse thrust?[J]. Economic Research Journal, 2013(3):83-95.
[2]王一鸣,李剑峰.我国债券市场收益率曲线影响因素的实证分析[J].金融研究,2005(1):111-124.Wang Y M, Li J F. An empirical analysis of the factors affecting the yield curve of China's bond market[J].Journal of Financial Research, 2005(1):111-124.
[3]牛霖琳,洪智武,陈国进.地方政府债务隐忧及其风险传导——基于国债收益率与城投债利差的分析[J].经济研究,2016(11):83-95.Niu L L, Hong Z W, Chen G J. The hidden worries of local government debt and its risk transmission—Based on the analysis of the deviation of government bond yield and city investment debt[J]. Economic Research Journal,2016(11):83-95.
[4]潘敏,夏庆,刘小燕,等.汇率制度改革、货币政策与国债利率期限结构[J].金融研究,2011(11):18-31.Pan M, Xia Q, Liu X Y, et al. Reform of exchange rate system, monetary policy and term structure of national debt interest rate[J]. Journal of Financial Research, 2011(11):18-31.
[5]刘澜飚,沈鑫,王博.中国宏观经济对国债利率期限结构的影响研究——基于动态随机一般均衡模型的分析[J].金融研究,2014(11):49-63.Liu L B, Shen X, Wang B. Research on the influence of China's macro-economy on the term structure of national debt interest rate—Based on the analysis of dynamic stochastic general equilibrium model[J]. Journal of Financial Research, 2014(11):49-63.
[6]Ang A, Piazzesi M. A no-arbitrage vector autoregression of term structure dynamics with macroeconomic and latent variables[J]. Journal of Monetary Economics, 2003(50):745-787.
[7]Connolly R, Stivers C, Sun L. Stock market uncertainty and the stock-bond return relation[J]. Journal of Financial&Quantitative Analysis, 2005(40):161-194.
[8]Baele L,Bekaert G, Inghelbrecht K. The determinants of stock and bond return comovements[J]. Review of Financial Studies, 2010(23):2374-2428.
[9]Hong Y, Lin H, Wu C. Are corporate bond market returns predictable?[J]. Journal of Banking&Finance,2012(36):2216-2232.
[10]Lin H, Wang J, Wu C. Predictions of corporate bond excess returns[J]. Journal of Financial Market, 2014(21):123-152.
[11]Chen L, Zhang G, Zhang W. Return predictions in the corporate bond market along the supply chain[J]. Journal of Financial Market, 2016(29):66-86.
[12]Phlillips P C B, Moon H. Nonstationary panel data analysis:An overview of some recent developments[J].Econometric Reviews, 2000(19):263-286.
[13]Phillips P C B, Lee J H. Predictive regression under various degrees of persistence and robust long-horizon regression[J]. Journal of Econometrics, 2013(177):250-264.
[14]Welch I, Goyal A. A comprehensive look at the empirical performance of equity premium prediction[J]. Review of Financial Studies, 2008(21):455-508.
[15]Stambaugh R F. Predictive regressions[J]. Journal of Financial Economics, 1999(54):375-421.
[16]Elliott G. On the robustness of cointegration methods when regressors almost have unit roots[J]. Econometrica,1998(66):149-158.
[17]袁裕泽.带有GARCH误差和趋势项的近单位根过程的估计[J].浙江大学学报(理学版),2014, 41(2):145-148.Yuan Y Z. Estimation of near-unit root processes with GARCH errors and trend terms[J]. Journal of Zhejiang University(Science Edition), 2014, 41(2):145-148.
[18]邓伟,唐齐鸣.单位根相关过程:理论的发展与比较[J].经济学动态,2014(2):132-141.Deng W, Tang Q M. Unit root related process:Theoretical development and comparison[J]. Economic Perspectives, 2014(2):132-141.
[19]史永东,何海江,沈德华.中国股市有效性动态变化的实证研究[J].系统工程理论与实践,2002, 22(12):88-92.Shi Y D, He H J, Shen D H. Empirical analysis of efficiency evolution in Chinese stock markets[J]. Systems Engineering—Theory&Practice, 2002, 22(12):88-92.
[20]孟庆芳,张强,牟文英.混沌序列自适应多步预测及在股票中的应用[J].系统工程理论与实践,2005, 25(12):62-68.Meng Q F, Zhang Q, Mu W Y. A novel adaptive multi-step-prediction method for chaotic time series and its applications in stock market[J]. Systems Engineering—Theory&Practice, 2005, 25(12):62-68.
[21]饶育蕾,彭叠峰,贾文静.交叉持股是否导致收益的可预测性?基于有限注意的视角[J].系统工程理论与实践,2013,33(7):1753-1761.Rao Y L, Peng D F, Jia W J. Does cross-shareholding cause return predictability? Based on limited attention perspective[J]. Systems Engineering—Theory&Practice, 2013, 33(7):1753-1761.
[22]陈蓉,赵永杰.隐含波动率曲面的预测研究:来自中国台湾市场的证据[J].系统工程理论与实践,2017, 37(8):1949-1962.Chen R, Zhao Y J. Forecasting implied volatility surface:Evidence from Taiwan China market[J]. Systems Engineering—Theory&Practice, 2017, 37(8):1949-1962.
[23]Phillips P C B, Magdalinos T. Econometric inference in the vicinity of unity[R]. CoFie Working Paper No.7,Singapore Management University, 2009.
[24]Cavanagh C L, Elliott G, Stock J H. Inference in models with nearly integrated regressors[J]. Econometric Theory,1995(11):1131-1147.
[25]Campbell J Y, Yogo M. Efficient tests of stock return predictability[J]. Journal of Financial Economics, 2006(81):27-60.
[26]Cai Z, Wang Y. Testing predictive regression models with nonstationary regressors[J]. Journal of Econometrics,2014(178):4-14.
[27]Chen W W, Deo R S, Yi Y. Uniform inference in predictive regression models[J]. Journal of Business&Economic Statistics, 2013(31):525-533.
[28]Kostakis A, Magdalinos T, Stamatogiannis M P. Robust econometric inference for stock return predictability[J].Review of Financial Studies, 2015(53):1506-1533.
[29]Francq C, Zakoian J M. GARCH models:Structure, statistical inference and financial applications[M]. Hoboken,NJ:Wiley, 2010.
[30]Boudoukh J, Mcallister P, Richardson M P. The valuation and hedging of deferred commission asset backed securities[J]. Social Science Electronic Publishing, 2008. DOI:http://hdl.handle.net/2451/26684.
[31]Ludvigson S C, Ng S. Macro factors in bond risk premia[J]. Review of Financial Studies, 2009(22):5027-5067.
[32]伍戈,曹红钢.中国的结构性通货膨胀研究——基于CPI与PPI的相对变化[J].金融研究,2014(6):1-16.Wu G, Cao H G. China's structural inflation research:Based on relative changes in CPI and PPI[J]. Journal of Financial Research, 2014(6):1-16.
[33]田开兰,孔亦舒,杨翠红.汇率波动对中国出口增加值的影响分析[J].系统工程理论与实践,2017, 37(5):1144-1153.Tian K L, Kong Y S, Yang C H. How much does exchange rate volatility affect China's value-added in exports?[J].Systems Engineering—Theory&Practice, 2017, 37(5):1144-1153.
[34]顾光同,许冰.中国工业增加值的半月预报:基于宏观月度数据[J].系统工程理论与实践,2018, 38(8):1983-1993.Gu G T, Xu B. Half-month forecast of China's industrial added value:Based on the macro monthly data[J].Systems Engineering—Theory&Practice, 2018, 38(8):1983-1993.
[35]杨子晖,赵永亮,柳建华.CPI与PPI传导机制的非线性研究:正向传导还是反向倒逼?[J].经济研究,2013(3):83-95.Yang Z H, Zhao Y L, Liu J H. Nonlinear study of CPI and PPI conduction mechanisms:Forward conduction or reverse thrust?[J]. Economic Research Journal, 2013(3):83-95.