  Bayesian inference for stochastic volatility models using MCMC methods highly
depends on actual parameter values in terms of sampling efficiency. While draws
from the posterior utilizing the standard centered parameterization break down
when the volatility of volatility parameter in the latent state equation is
small, non-centered versions of the model show deficiencies for highly
persistent latent variable series. The novel approach of
ancillarity-sufficiency interweaving has recently been shown to aid in
overcoming these issues for a broad class of multilevel models. In this paper,
we demonstrate how such an interweaving strategy can be applied to stochastic
volatility models in order to greatly improve sampling efficiency for all
parameters and throughout the entire parameter range. Moreover, this method of
"combining best of different worlds" allows for inference for parameter
constellations that have previously been infeasible to estimate without the
need to select a particular parameterization beforehand.
