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Bayesian Analysis of Stochastic Volatility Models

Author

Listed:
  • Jacquier, Eric
  • Polson, Nicholas G
  • Rossi, Peter E
Abstract
New techniques for the analysis of stochastic volatility models are developed. A Metropolis algorithm is used to construct a Markov Chain simulation tool. The exact solution to the filtering/smoothing problem of inferring about the unobserved variance states is a by-product of the authors' method. In addition, multistep-ahead predictive densities can be constructed. The authors illustrate their method by analyzing stock data. Sampling experiments are conducted to compare the performance of Bayes estimators to method of moments and quasi-maximum likelihood estimators proposed in the literature. In both parameter estimation and filtering, the Bayes estimators outperform these other approaches.

Suggested Citation

  • Jacquier, Eric & Polson, Nicholas G & Rossi, Peter E, 1994. "Bayesian Analysis of Stochastic Volatility Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(4), pages 371-389, October.
    • Handle: RePEc:bes:jnlbes:v:12:y:1994:i:4:p:371-89
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    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs

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