Bayesian semiparametric stochastic volatility modeling

Mark Jensen and John Maheu

No 2008-15, FRB Atlanta Working Paper from Federal Reserve Bank of Atlanta

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.

Keywords: Econometric models; Stochastic analysis (search for similar items in EconPapers)
Date: 2008
New Economics Papers: this item is included in nep-ets
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Citations: View citations in EconPapers (5)

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Related works:
Journal Article: Bayesian semiparametric stochastic volatility modeling (2010)
Working Paper: Bayesian Semiparametric Stochastic Volatility Modeling (2009)
Working Paper: Bayesian semiparametric stochastic volatility modeling (2008)
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