Online data processing: comparison of Bayesian regularized particle filters

TL;DR

This paper compares three Bayesian regularized particle filters for online data processing, focusing on filtering and parameter estimation within a stochastic volatility model, and finds that the regularized APF outperforms other methods.

Contribution

It provides a comparative analysis of regularized particle filters in an online setting, highlighting the superior performance of the regularized APF.

Findings

Regularized APF outperforms SIS and SIR in filtering accuracy.

Improper prior initialization impacts filter performance.

Bayesian regularized particle filters are effective for stochastic volatility models.

Abstract

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).