IDEAS home Printed from https://ideas.repec.org/h/eme/aecozz/s0731-9053(05)20030-0.html
   My bibliography  Save this book chapter

Bayesian Inference on Mixture-of-Experts for Estimation of Stochastic Volatility

In: Econometric Analysis of Financial and Economic Time Series

Author

Listed:
  • Alejandro Villagran
  • Gabriel Huerta

Abstract

The problem of model mixing in time series, for which the interest lies in the estimation of stochastic volatility, is addressed using the approach known as Mixture-of-Experts (ME). Specifically, this work proposes a ME model where the experts are defined through ARCH, GARCH and EGARCH structures. Estimates of the predictive distribution of volatilities are obtained using a full Bayesian approach. The methodology is illustrated with an analysis of a section of US dollar/German mark exchange rates and a study of the Mexican stock market index using the Dow Jones Industrial index as a covariate.

Suggested Citation

  • Alejandro Villagran & Gabriel Huerta, 2006. "Bayesian Inference on Mixture-of-Experts for Estimation of Stochastic Volatility," Advances in Econometrics, in: Econometric Analysis of Financial and Economic Time Series, pages 277-296, Emerald Group Publishing Limited.
  • Handle: RePEc:eme:aecozz:s0731-9053(05)20030-0
    DOI: 10.1016/S0731-9053(05)20030-0
    as

    Download full text from publisher

    File URL: https://www.emerald.com/insight/content/doi/10.1016/S0731-9053(05)20030-0/full/html?utm_source=repec&utm_medium=feed&utm_campaign=repec
    Download Restriction: Access to full text is restricted to subscribers

    File URL: https://www.emerald.com/insight/content/doi/10.1016/S0731-9053(05)20030-0/full/pdf?utm_source=repec&utm_medium=feed&utm_campaign=repec
    Download Restriction: Access to full text is restricted to subscribers

    File URL: https://libkey.io/10.1016/S0731-9053(05)20030-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    More about this item

    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:eme:aecozz:s0731-9053(05)20030-0. 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: Emerald Support (email available below). General contact details of provider: .

    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.