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Multivariate Stochastic Volatility via Wishart Processes - A Continuation

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  • Wolfgang Rinnergschwentner

    ()

  • Gottfried Tappeiner

    ()

  • Janette Walde

    ()

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    Abstract

    This paper picks up on a model developed by Philipov and Glickman (2006) for modeling multivariate stochastic volatility via Wishart processes. MCMC simulation from the posterior distribution is employed to fit the model. However, erroneous mathematical transformations in the full conditionals cause false implementation of the approach. We adjust the model, upgrade the analysis and investigate the statistical properties of the estimators using an extensive Monte Carlo study. Employing a Gibbs sampler in combination with a Metropolis Hastings algorithm inference for the time-dependent covariance matrix is feasible with appropriate statistical properties.

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    File URL: http://eeecon.uibk.ac.at/wopec2/repec/inn/wpaper/2011-19.pdf
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    Bibliographic Info

    Paper provided by Faculty of Economics and Statistics, University of Innsbruck in its series Working Papers with number 2011-19.

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    Length: 44
    Date of creation: Aug 2011
    Date of revision:
    Handle: RePEc:inn:wpaper:2011-19

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    Related research

    Keywords: Bayesian time series; Stochastic covariance; Timevarying correlation; Markov Chain Monte Carlo;

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    1. Philipov, Alexander & Glickman, Mark E., 2006. "Multivariate Stochastic Volatility via Wishart Processes," Journal of Business & Economic Statistics, American Statistical Association, vol. 24, pages 313-328, July.
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