Bayesian semiparameric multivariate stochastic volatility with an application to international volatility co-movements

Danielova Zaharieva Martina, Trede Mark, Wilfling Bernd

Abstract

In this paper, we establish a Cholesky-type multivariate stochastic volatility estimation framework, in which we let the innovation vector follow a Dirichlet process mixture, thus enabling us to model highly flexible return distributions. The Cholesky decomposition allows parallel univariate process modeling and creates potential for estimating highly 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 volatility co-movements among the largest stock markets.