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This paper presents a study using the Bayesian approach in stochasticvolatility models for modeling financial time series, using Hamiltonian MonteCarlo methods (HMC). We propose the use of other distributions for the errorsin the observation equation of stochastic volatility models, besides theGaussian distribution, to address problems as heavy tails and asymmetry in thereturns. Moreover, we use recently developed information criteria WAIC and LOOthat approximate the cross-validation methodology, to perform the selection ofmodels. Throughout this work, we study the quality of the HMC methods throughexamples, simulation studies and applications to real data sets.

Stochastic Volatily Models using Hamiltonian Monte Carlo Methods and Stan