The stylized facts of financial time series and the problem about unobserved component and time varying model parameters made the classical econometric model meet greate challenge in practice. The state space model, which is based on recursive bayesian filtering and combined with modern statistical methods, with its special model structure, provides a consistent analysis frame for an extensive issue of time series analysis.The dissertation investigates the theory and algorithms on state space modeling and tests their performance through simulation.Then discuss the applications in financial by empirical research. The researches are organized as following:1. Aiming at the problem about nonlinear, non-Gaussian, latent variable and time varying parameters in financial market, this dissertation sets up a research frame basen on the state space model.The advantage and disadvantage about those algorithms are discussed and some mothed to improve the performance are pointed out. Combining the recursive bayesian filtering with the EM algorithm, deduces the parameter estimation method, and form a consistent analysis frame for the problem in financial time series.2. The CAPM was rebuilt. (l)The time-varying betas of Chinese industrial stock returns were estimated using Kalman Filter, which overcomes the shortcoming of recursive regressions and rolling regressions. (2)VaR of the portfolio was estimated via combining the Sharpe diagonal model with Time-Varying beta.The result shows significant results.3.The Black-Scholes option pricing model was rebuilt. The variance rate was estimated taken as unobserved component and a nonlinear state-space model for warrant pricing was provided.Comparing the performance of the particle filter approach with the EWMA model and the implied volatilities model, the result suggests that the new approach is preferred.4. The classical return-based style analysis model was rebuilt and extended to a dynamic style analysis model. New model can consider the condition of linear and inequation restriction and was applied to an artificial portfolio and to return series of funds, the result suggests that the new approach is preferred.