Temporal Aggregation of Garch Processes
The authors derive low frequency, say weekly, models implied by high frequency, say daily, ARMA models with symmetric GARCH errors. They show that low frequency models exhibit conditional heteroskedasticity of the GARCH form as well. The parameters in the conditional variance equation of the low frequency model depend upon mean, variance, and kurtosis parameters of the corresponding high frequency model. Moreover, strongly consistent estimators of the parameters in the high frequency model can be derived from low frequency data. The common assumption in applications that rescaled innovations are independent is disputable, since it depends upon the available data frequency. Copyright 1993 by The Econometric Society.
(This abstract was borrowed from another version of this item.)
To our knowledge, this item is not available for
download. To find whether it is available, there are three
1. Check below under "Related research" whether another version of this item is available online.
2. Check on the provider's web page whether it is in fact available.
3. Perform a search for a similarly titled item that would be available.
|Date of creation:||1992|
|Date of revision:|
|Contact details of provider:|| Postal: TILBURG UNIVERSITY, CENTER FOR ECONOMIC RESEARCH, 5000 LE TILBURG THE NETHERLANDS.|
Phone: 31 13 4663050
Fax: 31 13 4663066
Web page: http://center.uvt.nl/
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:fth:tilbur:9240. 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: (Thomas Krichel)
If references are entirely missing, you can add them using this form.