English, Article, Journal or magazine article edition: Bayesian semiparametric stochastic volatility modeling Mark J. Jensen; John M. Maheu

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Bookmark: http://trove.nla.gov.au/version/25502
Physical Description
  • preprint
Language
  • English

Edition details

Title
  • Bayesian semiparametric stochastic volatility modeling
Author
  • Mark J. Jensen
  • John M. Maheu
Physical Description
  • preprint
Notes
  • 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. The new model is assessed based on simulation evidence, an empirical example, and comparison to parametric models.
  • Dirichlet process mixture, MCMC, block sampler
  • RePEc:tor:tecipa:tecipa-314
  • 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:
  • RePEc:rim:rimwps:wp23_09
  • 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, we use nonparametric Bayesian methods to flexibly model the skewness and kurtosis of the distribution while continuing to model the dynamics of volatility with a parametric structure. Our semiparametric Bayesian approach provides a full characterization of parametric and distributional uncertainty. We present a Markov chain Monte Carlo sampling approach to estimation with theoretical and computational issues for simulation from the posterior predictive distributions. The new model is assessed based on simulation evidence, an empirical example, and comparison to parametric models.
  • Econometric models ; Stochastic analysis
  • RePEc:fip:fedawp:2008-15
Language
  • English
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