Bayesian Semiparametric Stochastic Volatility Modeling

Mark Jensen and John Maheu

Working Paper series from Rimini Centre for Economic Analysis

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. An empirical example compares the new model to standard parametric stochastic volatility models.

Date: 2009-01
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.rcea.org/RePEc/pdf/wp23_09.pdf

Related works:
Journal Article: Bayesian semiparametric stochastic volatility modeling (2010)
Working Paper: Bayesian semiparametric stochastic volatility modeling (2008)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:rim:rimwps:23_09

Access Statistics for this paper

More papers in Working Paper series from Rimini Centre for Economic Analysis Contact information at EDIRC.
Bibliographic data for series maintained by Marco Savioli ().