The efficient Bayesian estimation method using Markov chain Monte Carlo is proposed
for a multivariate stochastic volatility model that is a natural extension of the
univariate stochastic volatility model with leverage and heavy-tailed errors, where we
further incorporate cross leverage effects among stock returns. Our method is based on
a multi-move sampler which samples a block of latent volatility vectors and is described
first in the literature for a multivariate stochastic volatility model with cross leverage
and heavy-tailed errors. Its high sampling efficiency is shown using numerical examples
in comparison with a single-move sampler which samples one latent volatility vector at
a time given other latent vectors and parameters. The empirical studies are given using
five dimensional stock return indices in Tokyo Stock Exchange.
|