An efficient Bayesian estimation using a Markov chain Monte Carlo method
is proposed in the case of a multivariate stochastic volatility model as a
natural extension of the univariate stochastic volatility model with leverage
and heavy-tailed errors. Note that we further incorporate cross-leverage
effects among stock returns. Our method is based on a multi-move sampler
that samples a block of latent volatility vectors. The method is presented
as a multivariate stochastic volatility model with cross leverage and heavytailed
errors. Its high sampling efficiency is shown using numerical examples
in comparison with a single-move sampler that samples one latent volatility
vector at a time, given other latent vectors and parameters. To illustrate the
method, empirical analyses are provided based on five-dimensional S&P500
sector indices returns.
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