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Bayesian Semiparametric Stochastic Volatility Modeling

Author

Listed:
  • Mark J. Jensen

    (Federal Reserve Bank of Atlanta)

  • John M. Maheu

    (University of Toronto and RCEA)

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.

Suggested Citation

  • Mark J. Jensen & John M. Maheu, 2009. "Bayesian Semiparametric Stochastic Volatility Modeling," Working Paper series 23_09, Rimini Centre for Economic Analysis.
    • Handle: RePEc:rim:rimwps:23_09
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    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General

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