Arbeitspapier
Bayesian semiparametric stochastic volatility modeling
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.
- Language
-
Englisch
- Bibliographic citation
-
Series: Working Paper ; No. 2008-15
- Classification
-
Wirtschaft
Bayesian Analysis: General
Semiparametric and Nonparametric Methods: General
Forecasting Models; Simulation Methods
- Subject
-
Bayesian nonparametrics
Dirichlet process mixture prior
Markov chain Monte Carlo
mixture models
stochastic volatility
- Event
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Geistige Schöpfung
- (who)
-
Jensen, Mark J.
Maheu, John M.
- Event
-
Veröffentlichung
- (who)
-
Federal Reserve Bank of Atlanta
- (where)
-
Atlanta, GA
- (when)
-
2008
- Handle
- Last update
-
10.03.2025, 11:41 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Jensen, Mark J.
- Maheu, John M.
- Federal Reserve Bank of Atlanta
Time of origin
- 2008
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