In this article, we establish a Cholesky-type multivariate stochastic volatility
estimation framework, in which we let the innovation vector follow a
Dirichlet process mixture (DPM), thus enabling us to model highly flexible
return distributions. The Cholesky decomposition allows parallel univariate
process modeling and creates potential for estimating high-dimensional
specifications. We use Markov chain Monte Carlo methods for posterior
simulation and predictive density computation. We apply our framework to
a five-dimensional stock-return data set and analyze international stockmarket co-movements among the largest stock markets. The empirical
results show that our DPM modeling of the innovation vector yields substantial gains in out-of-sample density forecast accuracy when compared
with the prevalent benchmark models
