Genetic Algorithm Sequential Monte Carlo Methods For Stochastic Volatility And Parameter Estimation

摘要

Particle filters are an important class of online posterior density estimation algorithms. In this paper we propose a real coded genetic algorithm particle filter (RGAPF) for the dual estimation of stochastic volatility and parameters of a Heston type stochastic volatility model. We compare the performance of our hybrid particle filter with a parameter learning particle filter present in literature. Our algorithm out performs this algorithm for both the volatility and parameter estimation.