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The sampling efficiency of MCMC methods in Bayesian inference for stochasticvolatility (SV) models is known to highly depend on the actual parametervalues, and the effectiveness of samplers based on different parameterizationsvaries significantly. We derive novel algorithms for the centered and thenon-centered parameterizations of the practically highly relevant SV model withleverage, where the return process and innovations of the volatility processare allowed to correlate. Moreover, based on the idea ofancillarity-sufficiency interweaving (ASIS), we combine the resulting samplersin order to guarantee stable sampling efficiency irrespective of the baselineparameterization.We carry out an extensive comparison to already existingsampling methods for this model using simulated as well as real world data.

Approaches Toward the Bayesian Estimation of the Stochastic Volatility Model with Leverage