  In this paper, We propose a new style panel data factor stochastic volatility
model with observable factors and unobservable factors based on the
multivariate stochastic volatility model, which is mainly composed of three
parts, such as the mean equation, volatility equation and factor volatility
evolution. The stochastic volatility equation is a 1-step forward prediction
process with high dimensional parameters to be estimated. Using the Markov
Chain Monte Carlo Simulation (MCMC) method, the Forward Filtering Backward
Sampling (FFBS) algorithm of the stochastic volatility equation is mainly used
to estimate the new model by Kalman Filter Recursive Algorithm (KFRA). The
results of numeric simulation and latent factor estimation show that the
algorithm possesses robustness and consistency for parameter estimation. This
paper makes a comparative analysis of the observable and unobservable factors
of internet finance and traditional financial listed companies in the Chinese
stock market using the new model and its estimation method. The results show
that the influence of observable factors is similar to the two types of listed
companies, but the influence of unobservable factors is obviously different.
