The objective of this paper is to explore the interlinkages among collateral monetary policy, shadow banking system and bank risks in China. The existing literature pays limited attention to the response of the shadow banking system towards collateral monetary policy in the past decade. This paper underscores the distorting effect of collateral monetary policy on the prosperity of the shadow banking system. In addition, this paper also highlights the effects of the New Asset Management Regulation (NAM Regulation) in 2018 which mitigates the stimulation effect of collateral monetary policy on the shadow banking system and bank risks.
This paper uses Shapley Additive Explanations (SHAP)-Bayesian-XGBoost machine learning methods to investigate the driving role of collateral monetary policy in shadow banking system and bank risks. XGBoost is an ensemble model built on an efficient implementation of decision trees, designed to produce a combined model with superior predictive performance. To interpret results and uncover the “black box” of machine learning in analyzing the relationships between collateral monetary policy, shadow banking system and bank risks, SHAP are employed.
First, the collateral monetary policy simulates the growth of the shadow banking system due to the distorting effect of collateral monetary policy on liquidity distribution. Second, the collateral monetary policy increases bank risks if it stimulates the shadow banking system. Third, the non-primary banks which receive limited liquidity from the central bank are more sensitive to collateral monetary policy and shadow banking system. Fourth, the NAM Regulation in 2018 mitigates the role of collateral monetary policy in the shadow banking system and bank risks.
First, this paper fills the research gap by exploring the distorting effect of collateral monetary policy on liquidity distribution and the shadow banking system. Second, it extends the understanding of bank risk by quantifying the responses of bank risk, derived from the negotiable certificate of deposit market to collateral monetary policy. Third, this paper employs the SHAP-XGBoost method to identify and attribute the role of collateral monetary policy in the shadow banking system and bank risk. Fourth, the study investigates how the relationship among collateral monetary policy, shadow banking system and bank risk evolves before and after the 2018 NAM Regulations.
