Bayesian semiparametric multivariate stochastic volatility with an application to international stock-market co-movements

Martina Danielova Zaharieva, Mark Trede () and Bernd Wilfling

No 6217, CQE Working Papers from Center for Quantitative Economics (CQE), University of Muenster

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 (DPM), thus enabling us to model highly exible return distributions. The Cholesky decomposition allows parallel univariate process modeling and creates potential for estimating high-dimensional speci cations. 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 forecst accuracy when compared with the prevalent benchmark models.

Keywords: Bayesian nonparametrics; Markov Chain Monte Carlo; Dirichlet process mixture; multivariate stochastic volatility; stock-market co-movements (search for similar items in EconPapers)
JEL-codes: C11 C14 C53 C58 G10 (search for similar items in EconPapers)
Pages: 44 pages
Date: 2017-06
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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