Bayesian semiparametric multivariate stochastic volatility with application

Martina Danielova Zaharieva, Mark Trede () and Bernd Wilfling

Econometric Reviews, 2020, vol. 39, issue 9, 947-970

Abstract: 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 stock-market 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.

Date: 2020
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DOI: 10.1080/07474938.2020.1761152

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