Sequential Monte Carlo Methods for Stochastic Volatility Models

In this paper we propose a sequential Monte Carlo algorithm to estimate a stochastic volatility model with leverage effect and non constant conditional mean. Our proposal relies on the auxiliary particle filter method that allows to sequentially evaluate the parameters of the latent processes involved in the dynamics of interest. An empirical application on simulated data is presented to study the performance of the implemented algorithm