Abstract: Liquidity in stock market is a crucial indicator to assess whether a country's stock market is in healthy operation. The academic research about liquidity is a hot issue on financial market microstructure theory. The influence of macroeconomic factors to time-varying liquidity in Chinese stock market is considered in this paper. First the indexes are selected to measure stock liquidity and macroeconomic covariates, and then three dynamic factor models are proposed for the panel data of industry sectors, with introducing a latent factor that captures the risk cross-sectionally clustering phenomenon to modeling. Moreover, liquidity is analyzed using the tools of state space model and Kalman filter, and the out-of-cample liquidity risk prediction of all the models is implemented. The novel Gaussian panel data time series model with regression effects is presented, for the analysis and forecast of stock liquidity risk, containing the principal components from a large number of macroeconomic covariates, which has a strong information mining effect. In an empirical application to stock data from Shanghai stock exchange A share market, it is found that a dynamic latent component or frailty factor is needed to prevent a downward bias in the estimation of liquidity.

Key words: liquidity, macroeconomic factors, state space model, Kalman filtering, latent factor