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Bayesian Semiparametric Stochastic Volatility Modeling

  • Mark J. Jensen

    ()

    ( Federal Reserve Bank of Atlanta)

  • John M. Maheu

    ()

    ( University of Toronto and RCEA)

This paper extends the existing fully parametric Bayesian literature on stochastic volatility to allow for more general return distributions. Instead of specifying a particular distribution for the return innovation, nonparametric Bayesian methods are used to flexibly model the skewness and kurtosis of the distribution while the dynamics of volatility continue to be modeled with a parametric structure. Our semiparametric Bayesian approach provides a full characterization of parametric and distributional uncertainty. A Markov chain Monte Carlo sampling approach to estimation is presented with theoretical and computational issues for simulation from the posterior predictive distributions. An empirical example compares the new model to standard parametric stochastic volatility modelsClassification-JEL:

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Paper provided by The Rimini Centre for Economic Analysis in its series Working Paper Series with number 23_09.

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Date of creation: Jan 2009
Date of revision: Jan 2009
Handle: RePEc:rim:rimwps:23_09
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  16. Mark J Jensen & John M Maheu, 2008. "Bayesian semiparametric stochastic volatility modeling," Working Papers tecipa-314, University of Toronto, Department of Economics.
  17. Jacquier, Eric & Polson, Nicholas G. & Rossi, P.E.Peter E., 2004. "Bayesian analysis of stochastic volatility models with fat-tails and correlated errors," Journal of Econometrics, Elsevier, vol. 122(1), pages 185-212, September.
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