IDEAS home Printed from https://ideas.repec.org/p/auc/wpaper/206.html
   My bibliography  Save this paper

BUGS for a Bayesian Analysis of Stochastic Volatility Models

Author

Listed:
  • Meyer, Renate
  • Yu, Jun

Abstract

This paper reviews the general Bayesian approach to parameter estimation in stochastic volatility models with posterior computations performed by Gibbs sampling. The main purpose is to illustrate the ease with which the Bayesian stochastic volatility model can now be studied routinely via BUGS (Bayesian Inference Using Gibbs Sampling), a recently developed, user-friendly, and freely available software package. It is an ideal software tool for the exploratory phase of model building as any modifications of a model including changes of priors and sampling error distributions are readily realized with only minor changes of the code. BUGS automates the calculation of the full conditional posterior distributions using a model representation by directed acyclic graphs. It contains an expert system for choosing an efficient sampling method for each full conditional. Furthermore, software for convergence diagnostics and statistical summaries is available for the BUGS output. The BUGS implementation of a stochastic volatility model is illustrated using a time series of daily Pound/Dollar exchange rates.

Suggested Citation

  • Meyer, Renate & Yu, Jun, 2000. "BUGS for a Bayesian Analysis of Stochastic Volatility Models," Working Papers 206, Department of Economics, The University of Auckland.
    • Handle: RePEc:auc:wpaper:206
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/2292/206
    Download Restriction: no
    ---><---

    Other versions of this item:

    More about this item

    Keywords

    Stochastic Volatility; Economics;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:auc:wpaper:206. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Library Digital Development (email available below). General contact details of provider: https://edirc.repec.org/data/deaucnz.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.