Sequential Parameter Estimation in Stochastic Volatility Jump-Diffusion Models

This paper considers the problem of sequential parameter and state estimation in stochastic volatility jump diffusion models. We describe the existing methods, the particle and practical filter, and then develop algorithms to apply these methods to the case of stochastic volatility models with jumps. We analyze the performance of both approaches using both simulated and S and P 500 index return data. On simulated data, we find that the algorithms are both effective in estimating jumps, volatility, and parameters. On S and P 500 index data, the practical filter appears to outperform the particle filter.