IDEAS home Printed from https://ideas.repec.org/p/cdl/anderf/qt93s6p8gb.html

Flexible Multivariate GARCH Modeling With an Application to International Stock Markets

Author

Listed:
  • Ledoit, Olivier
  • Santa-Clara, Pedro
  • Wolf, Michael

Abstract

We develop an estimation method for the Diagonal Multivariate GARCH model. For a vector of size N unidimensional GARCH processes for the diagonal elements of the conditional covariance matrix, and N(N-1)/2 bivariate GARCH processes for the off-diagonal elements of the conditional covariance matrix. The coefficient matrices are then transformed in such a way that ensures the positive semi-definiteness of the conditional covariance matrix. Under a technical assumption, the estimator has the same asymptotic properties as the univariate and bivariate maximum likelihood GARCH estimators. The method is computationally feasible for large problems, of size N=100 or larger. We do not need to impose any particular simplifying structure on the coefficient matrices. The conditional covariance is ensured to be stationary, and is, in general, well conditioned. We provide an empirical application in the context of international stock markets and offer Monte Carlo evidence of the good small sample properties of the model.

Suggested Citation

  • Ledoit, Olivier & Santa-Clara, Pedro & Wolf, Michael, 1999. "Flexible Multivariate GARCH Modeling With an Application to International Stock Markets," University of California at Los Angeles, Anderson Graduate School of Management qt93s6p8gb, Anderson Graduate School of Management, UCLA.
  • Handle: RePEc:cdl:anderf:qt93s6p8gb
    as

    Download full text from publisher

    File URL: https://www.escholarship.org/uc/item/93s6p8gb.pdf;origin=repeccitec
    Download Restriction: no
    ---><---

    Other versions of this item:

    More about this item

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

    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:cdl:anderf:qt93s6p8gb. 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: Lisa Schiff (email available below). General contact details of provider: https://edirc.repec.org/data/aguclus.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.