The establishment of a sensitive and forward-looking systemic financial risk monitoring system represents a crucial prerequisite for the maintenance of financial stability and the prevention of systemic financial risks.This paper introduces the commodity price index and the real effective exchange rate index into the market instability risk index and introduces the bank liquidity index into the market liquidity risk index,intending to construct a four-dimensional systemic financial risk indicator system.Subsequently,the systemic financial risk monitoring index is calculated using the principal component analysis method.The sensitivity and foresight of this index are then tested using methods such as the long short-term memory neural network.The results reveal that the systemic financial risk monitoring index,which incorporates a range of indicators including the commodity price index,the real effective foreign exchange index,and bank liquidity,demonstrates a heightened sensitivity and forward-looking risk monitoring capacity.The predicted and true values of the systemic financial risk monitoring index demonstrate overall consistency in changes under both the long-and short-term neural network models.The index exhibits good foresight regarding risk indicators such as the bank's risk-weighted asset ratio,non-performing loan ratio,and provision coverage ratio.It can provide risk warnings one week to one quarter in advance.The findings of this study provide some reference for further enhancements to the systemic financial risk monitoring system.
Systemic Financial RiskMonitoring SystemDeep LearningNeural Network Model
NETL
NSTL
万方数据
