Sequential Monte Carlo Methods for Stochastic Volatility Models with Jumps

In this paper we propose a sequential Monte Carlo algorithm to estimate a stochastic volatility model with leverage eect, non constant conditional mean and jumps. Our idea relies on the auxiliary particle lter algorithm together with the Markov Chain Monte Carlo (MCMC) method- ology. Our method allows to sequentially evaluate the parameters and the latent processes involved in the dynamic of interest. An empirical applica- tion on simulated data and on the Standard & Poor's 500 index is presented to study the performance of the algorithm implemented.

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