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Bayesian semiparametric stochastic volatility modeling

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  • Mark J Jensen
  • John M Maheu

Abstract

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.

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Bibliographic Info

Paper provided by University of Toronto, Department of Economics in its series Working Papers with number tecipa-314.

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Length: 51 pages
Date of creation: 25 Apr 2008
Date of revision:
Handle: RePEc:tor:tecipa:tecipa-314

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Keywords: Dirichlet process mixture; MCMC; block sampler;

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