Hybrid metaheuristic particle filters for stochastic volatility estimation

Abstract

In this paper we propose hybrid metaheuristic particle filters for the dual estimation of state and parameters in a stochastic volatility estimation problem. We use evolutionary strategies and real coded genetic algorithms as the metaheuristics. The hybrid metaheuristic particle filters provides more accurate results and uses lesser number of particles for this high dimension estimation problem. We compare the performance of our hybrid algorithms with a sequential importance resampling particle filter (SIR) and the parameter learning algorithm (PLA). Our hybrid particle filters out perform both these algorithms for this particular dual estimation problem.