IDEAS home Printed from https://ideas.repec.org/a/asi/aeafrj/v3y2013i4p553-559id1019.html
   My bibliography  Save this article

SV Mixture, Classification Using EM Algorithm

Author

Listed:
  • Ahmed Hachicha
  • Fatma Hachicha
  • Afif Masmoudi

Abstract

The present paper presents a theoretical extension of our earlier work entitled“A comparative study of two models SV with MCMC algorithm” cited, Rev Quant Finan Acc (2012) 38:479-493 DOI 10.1007/s11156-011-0236-1 where we propose initially a mixture stochastic volatility model providing a tractable method for capturing certain market characteristics. To estimate the parameter of a mixture stochastic volatility model, we first use the Expectation-Maximisation (EM) algorithm. The second step is to adopt the clustering approach to classify the database into subsets (clusters).

Suggested Citation

  • Ahmed Hachicha & Fatma Hachicha & Afif Masmoudi, 2013. "SV Mixture, Classification Using EM Algorithm," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 3(4), pages 553-559.
    • Handle: RePEc:asi:aeafrj:v:3:y:2013:i:4:p:553-559:id:1019
    as

    Download full text from publisher

    File URL: https://archive.aessweb.com/index.php/5002/article/view/1019/1501
    Download Restriction: no
    ---><---

    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:asi:aeafrj:v:3:y:2013:i:4:p:553-559:id:1019. 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: Robert Allen (email available below). General contact details of provider: https://archive.aessweb.com/index.php/5002/ .

    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.