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Bayesian semiparametric stochastic volatility modeling

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Author Info
Mark J Jensen
John M Maheu

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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.

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Publisher Info
Paper provided by University of Toronto, Department of Economics in its series Working Papers with number tecipa-314.

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Length: 51 pages
Date of creation: 25 Apr 2008
Date of revision:
Handle: RePEc:tor:tecipa:tecipa-314

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Related research
Keywords: Dirichlet process mixture; MCMC; block sampler;

Other versions of this item:

Find related papers by JEL classification:
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions
C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Bayesian Analysis

This paper has been announced in the following NEP Reports:

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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  3. Mark J. Jensen, 2004. "Semiparametric Bayesian Inference of Long-Memory Stochastic Volatility Models," Journal of Time Series Analysis, Blackwell Publishing, vol. 25(6), pages 895-922, November. [Downloadable!] (restricted)
  4. Durham, Garland B., 2006. "Monte Carlo methods for estimating, smoothing, and filtering one- and two-factor stochastic volatility models," Journal of Econometrics, Elsevier, vol. 133(1), pages 273-305, July. [Downloadable!] (restricted)
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    Other versions:
  7. Bjørn Eraker & Michael Johannes & Nicholas Polson, 2003. "The Impact of Jumps in Volatility and Returns," Journal of Finance, American Finance Association, vol. 58(3), pages 1269-1300, 06. [Downloadable!] (restricted)
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    Other versions:
  14. Yu, Jun, 2005. "On leverage in a stochastic volatility model," Journal of Econometrics, Elsevier, vol. 127(2), pages 165-178, August. [Downloadable!] (restricted)
    Other versions:
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  19. Omori, Yasuhiro & Chib, Siddhartha & Shephard, Neil & Nakajima, Jouchi, 2007. "Stochastic volatility with leverage: Fast and efficient likelihood inference," Journal of Econometrics, Elsevier, vol. 140(2), pages 425-449, October. [Downloadable!] (restricted)
  20. Blattberg, Robert C & Gonedes, Nicholas J, 1974. "A Comparison of the Stable and Student Distributions as Statistical Models for Stock Prices," Journal of Business, University of Chicago Press, vol. 47(2), pages 244-80, April. [Downloadable!] (restricted)
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    Other versions:
  26. Harvey, Andrew & Ruiz, Esther & Shephard, Neil, 1994. "Multivariate Stochastic Variance Models," Review of Economic Studies, Blackwell Publishing, vol. 61(2), pages 247-64, April. [Downloadable!] (restricted)
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Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Martin Burda & Matthew Harding & Jerry Hausman, 2008. "A Bayesian mixed logit-probit model for multinomial choice," CeMMAP working papers CWP23/08, Centre for Microdata Methods and Practice, Institute for Fiscal Studies. [Downloadable!]
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