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Matrix-State Particle Filter for Wishart Stochastic Volatility Processes

Listed author(s):
  • Roberto Casarin

    ()

    (Department of Economics, University Of Venice C� Foscari)

  • Domenico Sartore

    (Department of Economics, University Of Venice C� Foscari)

This work deals with multivariate stochastic volatility models, which account for a time-varying variance-covariance structure of the observable variables. We focus on a special class of models recently proposed in the literature and assume that the covariance matrix is a latent variable which follows an autoregressive Wishart process. We review two alternative stochastic representations of the Wishart process and propose Markov-Switching Wishart processes to capture different regimes in the volatility level. We apply a full Bayesian inference approach, which relies upon Sequential Monte Carlo (SMC) for matrix-valued distributions and allows us to sequentially estimate both the parameters and the latent variables.

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File URL: http://www.unive.it/media/allegato/DIP/Economia/Working_papers/Working_papers_2007/WP_DSE_casarin_sartore_30_07.pdf
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Paper provided by Department of Economics, University of Venice "Ca' Foscari" in its series Working Papers with number 2007_30.

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Length: 16
Date of creation: 2007
Handle: RePEc:ven:wpaper:2007_30
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  16. Asai, Manabu & McAleer, Michael, 2009. "The structure of dynamic correlations in multivariate stochastic volatility models," Journal of Econometrics, Elsevier, vol. 150(2), pages 182-192, June.
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