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Bayesian semiparametric multivariate stochastic volatility with an application to international stock-market co-movements
| Content Provider | Semantic Scholar |
|---|---|
| Author | Zaharieva, Martina Danielova Trede, Mark Wilfling, Bernd |
| Copyright Year | 2017 |
| 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. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://www.wiwi.uni-muenster.de/cqe/sites/cqe/files/CQE_Paper/cqe_wp_62_2017.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
