The validity of CAPM is contested by several studies based on factor models. During the last decades, aiming to explain the known financial anomalies of stock returns, two major lines of research emerged: the use of asset pricing models that allow for multiple sources of risk – the multifactor models – as well as the dynamic approach to model the sensitivities of returns in respect to the risk factors – the conditional models. The conditional models, based on one or more risk factors, explicit the expected return conditional to the information set available in the previous period. The factor sensitivities, or the betas, are estimated as dynamic parameters according to different approaches in the literature. The main objective in this thesis is to study conditional pricing models based on state-space approach. The betas dynamics are described as stochastic processes and estimated through the Kalman filter in order to verify the models ability to explain the returns and related financial anomalies, such as size and value effects. Two empirical applications are presented: one for Conditional CAPM in the Brazilian stock market and another for Conditional Fama and French (1993) three-factor model in the American stock market. In both cases, time-varying sensitivities treatment provides better model adjustment as well as smaller pricing errors compared to correspondent unconditional models.