Inference methods for stochastic volatility models

In the present paper we consider estimation procedures for stationaryStochastic Volatility models, making inferences about the latent volatilityof the process. We show that a sequence of generalized least squaresregressions enables us to determine the estimates. Finally, we makeinferences iteratively by using the Kalman Filter algorithm.