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