Research on Early Warning of Credit Bond Liquidity Risk Based on Machine Learning
Preventing and controlling the credit bond liquidity risk is crucial for preventing systemic financial risks and safeguarding national security,making it the top priority in financial operations.The purpose of this paper is to develop effec-tive measures for measuring and early warning credit bond liquidity risk.These measures can provide assistances in completing the important task of improving the financial regulatory system and guarding against systemic financial risks.Using the monthly data of China's credit bonds from January 2009 to December 2020,this paper measures the liquidity risk of credit bonds through tail correlation,constructs an early-warning risk factor system from the perspective of financing constraint,credit risk and noise trading.Using 11 kinds of machine learning models including neural network to early-warn li-quidity risk of credit bonds,and identify the mechanism of key risk factors.The findings of this paper are as follows.Firstly,the neural network with one hidden layer has better early warning ability on the liquidity risk of credit bonds.It has strong early warning stability in different types of bonds and different external envi-ronment samples,being able to accurately warn the liquidity depletion events at the market level.Secondly,bond age is the core risk factor driving liquidity risk.Newly issued bonds will form liquidity risk by causing noise trading.With the increase of bond age,liquidity risk will only decline in a decreasing manner.Thirdly,liquidity risk is generated by the coordinated move-ment of multiple types of risk factors.Among them,the nonlinear linkage of changes in economic conditions,changes in mon-etary policy,cross market impact and bond age serve as the most important factors that may drive liquidity risk.
NETL
NSTL
万方数据
