Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/70634
Authors: 
Jensen, Mark J.
Maheu, John M.
Year of Publication: 
2008
Series/Report no.: 
Working Paper, Federal Reserve Bank of Atlanta 2008-15
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, 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.
Subjects: 
Bayesian nonparametrics
Dirichlet process mixture prior
Markov chain Monte Carlo
mixture models
stochastic volatility
JEL: 
C11
C14
C53
Document Type: 
Working Paper

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