Particle filtering based recovery of noisy GARCH processes

In this paper, we address the problem of enhancement of a noisy GARCH process using a particle filter. We compare our approach experimentally to a previously developed recursive estimation scheme. Simulations indicate that a significant gain in performance is obtained, at the cost of higher sensitivity to errors in the GARCH parameters. The proposed method allows tackling arbitrary driving noise distributions as well as arbitrary fidelity criteria.