Abstract: This paper extends the existing fully parametric Bayesian literature on stochastic volatility to allow for more general return distributions. Instead of specifying a particular
distribution for the return innovation, nonparametric Bayesian
methods are used to flexibly model the skewness and kurtosis of the
distribution while the dynamics of volatility continue to be modeled
with a parametric structure. Our semiparametric Bayesian approach
provides a full characterization of parametric and distributional
uncertainty. A Markov chain Monte Carlo sampling approach to
estimation is presented with theoretical and computational issues for
simulation from the posterior predictive distributions. The new model
is assessed based on simulation evidence, an empirical example, and
comparison to parametric models.
Keywords: Dirichlet process mixture, MCMC, block sampler
JEL Classification: C22; C11