  The aim of this paper is to compare three regularized particle filters in an
online data processing context. We carry out the comparison in terms of hidden
states filtering and parameters estimation, considering a Bayesian paradigm and
a univariate stochastic volatility model. We discuss the use of an improper
prior distribution in the initialization of the filtering procedure and show
that the regularized Auxiliary Particle Filter (APF) outperforms the
regularized Sequential Importance Sampling (SIS) and the regularized Sampling
Importance Resampling (SIR).
