Small-sample Properties of Estimators in an ARCH(1) and GARCH(1,1) Model with a Generalized Error Distribution: a Robustness Study
GARCH Models have become a workhouse in volatility forecasting of financial and monetary market time series. In this article, we assess the small sample properties in estimation and the performance in volatility forecasting of four competing distribution free methods, including quasi-maximum likelihood and three regression based methods. The study is carried out by means of Monte Carlo simulations. To guarantee an utmost realistic framework, simulated time series are generated from a mixture of two symmetric generalized error distributions. This data generating process allow to reproduce the stylized facts of financial time series, in particular, peakedness and skewness. The results of the study suggest that regression based methods can be an asset in volatility forecasting, since model parameters are subject to structural change over time and the efficiency of the quasi- maximum likelihood method is confined to large sample sizes. Furthermore, the good performance of forecasts based on the historical volatility supports to use the variance targeting method for volatility forecasting.
|Date of creation:||06 Dec 2005|
|Contact details of provider:|| Postal: Rolandstrasse 8, 49069 Osnabrueck|
Web page: http://www.iew.uni-osnabrueck.de/
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:iee:wpaper:wp0073. 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: (Karin Wessler-Rensmann)
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.