Bayesian semiparametric stochastic volatility modeling

Mark Jensen and John Maheu

Working Papers from University of Toronto, Department of Economics

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.

Keywords: Dirichlet process mixture; MCMC; block sampler (search for similar items in EconPapers)
JEL-codes: C11 C22 (search for similar items in EconPapers)
Pages: 51 pages
Date: 2008-04-25
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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https://www.economics.utoronto.ca/public/workingPapers/tecipa-314.pdf Main Text (application/pdf)

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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Persistent link: https://EconPapers.repec.org/RePEc:tor:tecipa:tecipa-314

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