Advanced Search
MyIDEAS: Login to save this article or follow this journal

Volatility Components and Long Memory-Effects Revisited

Contents:

Author Info

  • Haas Markus

    ()
    (University of Munich)

Abstract

The goal of this paper is to illuminate the capability of the component GARCH model of Ding and Granger (1996) and Engle and Lee (1999) to reproduce the long memory-type behavior of financial volatility. The potential of this model to capture the long memory dynamics observed in measures of financial volatility has been documented recently by Maheu (2005) and Deo et al. (2006), who base their conclusions on simulation techniques and a forecasting exercise, respectively. In this paper, a simple explanation for these observations is provided, which is based on the theoretical autocorrelation function (ACF) of the component GARCH model. We also elucidate why even higher-order GARCH models with Bollerslev's (1986) nonnegativity constraints enforced cannot mimic the long memory effects. The reasoning is supported with several empirical examples, for which we explicitly calculate the theoretical ACF implied by a couple of different fitted models, and find that their structure is just as predicted by our argument. To conveniently conduct these computations, a general simple method for computing the theoretical ACF of GARCH models is suggested, which is easier to use than the formulas developed so far, and particularly so for higher lag-orders. The ability of the component model to approximate long memory is also validated on the basis of a visual comparison between the empirical and the implied theoretical ACFs.

Download Info

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
File URL: http://www.degruyter.com/view/j/snde.2007.11.2/snde.2007.11.2.1411/snde.2007.11.2.1411.xml?format=INT
Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.

As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.

Bibliographic Info

Article provided by De Gruyter in its journal Studies in Nonlinear Dynamics & Econometrics.

Volume (Year): 11 (2007)
Issue (Month): 2 (May)
Pages: 1-39

as in new window
Handle: RePEc:bpj:sndecm:v:11:y:2007:i:2:n:3

Contact details of provider:
Web page: http://www.degruyter.com

Order Information:
Web: http://www.degruyter.com/view/j/snde

Related research

Keywords:

References

No references listed on IDEAS
You can help add them by filling out this form.

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as in new window

Cited by:
  1. Amendola, Alessandra & Storti, Giuseppe, 2008. "A GMM procedure for combining volatility forecasts," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 3047-3060, February.
  2. Haas, Markus & Mittnik, Stefan & Paolella, Marc S., 2008. "Asymmetric multivariate normal mixture GARCH," CFS Working Paper Series 2008/07, Center for Financial Studies (CFS).

Lists

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

Statistics

Access and download statistics

Corrections

When requesting a correction, please mention this item's handle: RePEc:bpj:sndecm:v:11:y:2007:i:2:n:3. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Peter Golla).

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.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with 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 profile, as there may be some citations waiting for confirmation.

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