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In this work, we propose a model for estimating volatility from financialtime series, extending the non-Gaussian family of space-state models with exactmarginal likelihood proposed by Gamerman, Santos and Franco (2013). On theliterature there are models focused on estimating financial assets risk,however, most of them rely on MCMC methods based on Metropolis algorithms,since full conditional posterior distributions are not known. We present analternative model capable of estimating the volatility, in an automatic way,since all full conditional posterior distributions are known, and it ispossible to obtain an exact sample of parameters via Gibbs Sampler. Theincorporation of jumps in returns allows the model to capture speculativemovements of the data, so that their influence does not propagate tovolatility. We evaluate the performance of the algorithm using synthetic andreal data time series.  Keywords: Financial time series, Stochastic volatility, Gibbs Sampler,Dynamic linear models.

Non-Gaussian Stochastic Volatility Model with Jumps via Gibbs Sampler