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