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This article investigates the dynamic changes in liquidity stratification in financial markets, emphasizing how time series analysis can be used to forecast market liquidity. In China's financial markets, since 2014, liquidity stratification has become crucial, leading to significant differences in borrowing costs and difficulties among financial firms. Focusing on small and medium-sized banks, this study explores the origins and consequences of liquidity stratification. Moreover, it provides a comprehensive analytical approach for predicting market liquidity. This framework includes Granger causality tests, cointegration analysis, Auto regressive Integrated Moving Average (ARIMA) model, and Vector Auto regression (VAR) model. The method is rooted in a sequential, logical progression and is it eratively refined based on model outcomes. The findings of this study offer insights into the complexity of liquidity in financial markets and present a robust methodology for financial analysts and policymakers to forecast and strategize under the influence of liquidity stratification.
Xu et al. (Thu,) studied this question.