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In this paper, we introduce efficient ensemble Markov Chain Monte Carlo(MCMC) sampling methods for Bayesian computations in the univariate stochasticvolatility model. We compare the performance of our ensemble MCMC methods withan improved version of a recent sampler of Kastner and Fruwirth-Schnatter(2014). We show that ensemble samplers are more efficient than this state ofthe art sampler by a factor of about 3.1, on a data set simulated from thestochastic volatility model. This performance gain is achieved without theensemble MCMC sampler relying on the assumption that the latent process islinear and Gaussian, unlike the sampler of Kastner and Fruwirth-Schnatter.

Efficient Bayesian inference for stochastic volatility models with ensemble MCMC methods