Analysis Of The Influence Of American Macroeconomic Variables On The Term Structure Of Chinese Green Bond Interest Rate

Since the beginning of this century,countries around the world have listed green and environmentally friendly development as an important topic in political and economic negotiations;The green economy has become a new economic trend and a new driving force for world economic growth.The introduction of the Equator Principles combines green environmental protection with financial development,and green finance,green credit,green bonds,etc.are gradually integrated into the process of economic development and transformation of countries.China has clearly put forward the dual carbon goal of achieving green economic development.It is the "blood" and "catalyst" of green development,and green finance will play an irreplaceable important role.The term structure of interest rate of green bonds is an important basis and reference factor for asset pricing of green finance,and plays a cornerstone role.Therefore,the research on the influencing factors of the term structure of interest rate of green bonds has always been a hot issue for scholars at home and abroad.This article aims to explore macroeconomic variables that have a significant impact on the term structure of green bond interest rates in China,and consider whether the fitting prediction accuracy of different step size models has improved after adding these macroeconomic variables.This article summarizes the importance of fitting and predicting the term structure of interest rates,the impact of various macroeconomic variables at home and abroad,and the methods of using neural network models to fit and predict them.This paper summarizes the three classical theories of the term structure of interest rates,and theoretically explains the relationship between the two macroeconomic variables,inflation rate and unemployment rate,and the term structure of green bond interest rates in China with the help of Phillips curve,Taylor rule,Fisher effect and its four paradoxes.This article selects the TVP-SV-VAR model for impact analysis,and uses the LTSM neural network model for fitting prediction after completing the impact analysis.We selected daily green bond yield data from November 2,2020 to November 19,2022,and used the TVP-SV-VAR model to explore and study the relationship between macroeconomic variables such as inflation and unemployment rates in the United States and the term structure of green bond interest rates in China.The research results show that the inflation and unemployment rates in the United States will have an impact on the interest rate term structure of China’s green bonds.The former has a positive pulse effect on China’s green bond interest rate term structure;The latter produces a negative pulse effect.This article uses the LSTM neural network model to fit and predict the term structure of interest rates.The inflation rate and unemployment rate of the United States are added as eigenvalues to the model,and compared with the model without the above eigenvalues.The comparison of the fitting and prediction results shows that the macroeconomic variables have a significant improvement in the fitting and prediction effect of long-term green bond interest rate term structure,but will reduce the fitting and prediction effect of short-term steps.Based on the empirical results,targeted suggestions were proposed for the future development of the green bond market.