  The sampling efficiency of MCMC methods in Bayesian inference for stochastic
volatility (SV) models is known to highly depend on the actual parameter
values, and the effectiveness of samplers based on different parameterizations
varies significantly. We derive novel algorithms for the centered and the
non-centered parameterizations of the practically highly relevant SV model with
leverage, where the return process and innovations of the volatility process
are allowed to correlate. Moreover, based on the idea of
ancillarity-sufficiency interweaving (ASIS), we combine the resulting samplers
in order to guarantee stable sampling efficiency irrespective of the baseline
parameterization.We carry out an extensive comparison to already existing
sampling methods for this model using simulated as well as real world data.
