中国股债市场长期动态相关性的影响因素研究——基于宏观不确定性和协动性视角

本文借鉴现代宏观经济学中的无套利仿射模型,基于"定价核"的定价方式,将股票市场和债券市场收益率之间的相关系数分解为其主要驱动因素--通货膨胀、真实利率和股息率的不确定性,以及三者之间的协方差。在实证部分,采用DCC-MGARCH模型计算股票市场和债券市场收益率的动态相关系数,验证中国股债相关性的时变规则;进而通过回归分析探究所选取的解释变量对中国股债相关性的贡献。结果表明,通货膨胀和股息率的不确定性以及真实利率与通货膨胀和股息率各自之间的协动性是影响这种相关性的主要因素;通胀冲击、真实利率和股息率可以解释这种相关性与长期动态的暂时背离。其中,通货膨胀和股息率的不确定性对股债相关性的影响与其他欧美主要经济体有着不同的表现,反映了中国市场的特殊性;此外,相较于中国经济市场的平稳时期,股市动荡期间各经济因素的影响会发生改变,且模型解释力会降低。

Research on the Influencing Factors of Long-term Dynamic Correlation of Chinese Stock and Bond Market--Based on the Perspective of Macroeconomic Uncertainty and Synchronization

Based on non-arbitrage affine model of modern macro-economics and pricing method of"pricing kernel",this paper splits the correlation coefficient between stock market and bond market yield into its main driving factors--the uncertainty of inflation,real interest rate,dividend rate and the covariance among the three mentioned above.In the empirical part,firstly,DCC-MGARCH model is used to calculate the dynamic correlation coefficient of stock market and bond market yields and verify the time-varying rule of correlation between Chinese stocks and bonds;then,the paper explores the contribution of the selected explanatory variables to the correlation between Chinese stocks and bonds through regression analysis.The results show that the uncertainty of inflation real interest rate and dividend yield,and the covariance among the three are the main factors affecting the correlation;inflation shocks,real interest rates and dividend yields can explain the temporary divergence from long-term dynamics.Among them,the impact of the uncertainty of inflation and dividend yield on the correlation between stocks and bonds is different from that of other major European and American countries,which reflects the particularity of the Chinese economic market.In addition,compared with the stable period of China’s economic market,the influence of these economic factors will change during the turbulent period of stock market,and the explanatory power of the model will be reduced.