Title: Efficient Bayesian estimation of the stochastic volatility model with leverage
Authors: Darjus Hosszejni - WU Vienna University of Economics and Business (Austria) [presenting]
Gregor Kastner - WU Vienna University of Economics and Business (Austria)
Abstract: 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 differs significantly. We derive novel samplers for the centered and the non-centered parameterizations of the practically highly relevant SV model with leverage, where the return process and the innovations of the volatility process are allowed to correlate. Moreover, based on the idea of ancillarity-sufficiency interweaving, we combine the resulting samplers in order to achieve superior sampling efficiency, irrespective of the baseline parameterization. The method is implemented using R and C++. Furthermore, we carry out an extensive comparison to already existing sampling methods for this model.