Stochastic volatility modeling with computational intelligence particle filters

Abstract

Stochastic volatility estimation is an important task for correctly pricing derivatives in mathematical finance. Such derivatives are used by varying types of market participant as either hedging tools or for bespoke market exposure. We evaluate our adaptive path particle filter, a recombinatory evolutionary algorithm based on the generation gap concept from evolutionary computation, for stochastic volatility estimation of three real financial asset time series. We calibrate the Heston stochastic volatility model employing a Markov-chain Monte Carlo, enabling us to understand the latent stochastic volatility process and parameters. In our experiments we find the adaptive path particle filter to be superior to the standard sequential importance resampling particle filter, the Markov-chain Monte Carlo particle filter and the particle learning particle filter. We present a detailed analysis of the results and suggest directions for future research.