We consider the problem of estimating stochastic volatility from stock data. The estimation of the volatility process of the Heston model is not in the usual framework of the filtering theory. Discretizing the continuous Heston model to the discrete-time one, we can derive the exact volatility filter and realize this filter with the aid of particle filter algorithm. In this paper, we derive the optimal importance function and construct the particle filter algorithm for the discrete-time Heston model. The parameters contained in system model are also estimated by constructing the augmented states for the system and parameters. The developed method is applied to the real data (AEX index).
|Number of pages||11|
|Journal||International journal of innovative computing, information and control|
|Publication status||Published - Jan 2009|
- Particle filter
- Stochastic volatility
- Parameter estimation
- Heston model