Abstract
We study the identification problem for Heston stochastic volatility model, which is widely used as a model of a stock in finance. Based on observation data (log price), we estimate the stochastically moving volatility with its systems parameters by using particle filtering technique.In order to apply the particle filter algorithm, we need to convert the original continuous model to a discrete one. In this paper, noting that the volatility process of Heston model can be generated from the non-central chi-square distribution, the exact particle filter algorithm is applicable. We also demonstrated some simulation studies for showing the efficiency of our proposed algorithm.
