Abstracts

In this paper, a new technique for the estimation of parameters for the stochastic volatility model is developed. Although several techniques including Markov chain Monte Carlo were applied and developed for this problem, most of their results were very sensitive for the initial value or random seed selection. Because of the problems mentioned above, it was difficult to apply those developed method directly to the real data. In the proposed method, unlikely in the original Markov chain Monte Carlo methods, the parameters are separated by some criteria and estimated iteratively like many other iterative methods such as expectation-maximization algorithm and this makes robust results. Applied to the artificial datasets, the proposed methods found the parameters and those results were robust compared to the parameters from the original Markov chain Monte Carlo. Additionally, it is successfully applied to the real market data including several indices and stock prices.