摘要
It’s the basic premise of promoting the healthy development of rural finance and strengthen-ing macro-prudential supervision to measure the systemic risk of rural finance accurately.We establish the dynamic factor CAPM and make an all-round and multi-angle quantitative study on the systemic risk of rural finance in China by constructing macro-micro index system and using machine learning to reduce the dimension of high-dimensional data.Our results show that the dynamic factor CAPM of using macro-micro big data can evaluate systemic risk of rural finance more comprehensively and systematically,and machine learning performs well in processing high-dimensional data.In addition,China’s rural financial systemic risk is stable compared with the Shanghai and Shenzhen main markets,but it is also susceptible to macro and micro influ-enced factors.Finally,it is pointed out that the early warning system of rural financial systemic risk could be constructed at macro and micro level,respectively.
