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

Author

Listed:
  • Martina Danielova Zaharieva
  • Mark Trede
  • Bernd Wilfling

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.

Suggested Citation

  • Martina Danielova Zaharieva & Mark Trede & Bernd Wilfling, 2017. "Bayesian semiparametric multivariate stochastic volatility with an application to international stock-market co-movements," CQE Working Papers 6217, Center for Quantitative Economics (CQE), University of Muenster.
  • Handle: RePEc:cqe:wpaper:6217
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    References listed on IDEAS

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    Cited by:

    1. Audrone Virbickaite & Hedibert F. Lopes, 2018. "Bayesian Semi-Parametric Markov Switching Stochastic Volatility Model," DEA Working Papers 89, Universitat de les Illes Balears, Departament d'Economía Aplicada.
    2. Audronė Virbickaitė & Hedibert F. Lopes, 2019. "Bayesian semiparametric Markov switching stochastic volatility model," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 35(4), pages 978-997, July.

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    More about this item

    Keywords

    Bayesian nonparametrics; Markov Chain Monte Carlo; Dirichlet process mixture; multivariate stochastic volatility; stock-market co-movements;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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