Augmented ARCH models for financial time series: stability conditions and empirical evidence
The class of conditionally heteroscedastic models known as 'augmented ARCH' encompasses most liear 'ARCH'-type models found in the literature and, in particular, two basic ARCH variants for autocorrelated series: Engle (1982) explains conditional variance by lagged errors, Weiss (1984) also by lagged observations. The framework permits an evaluation of whether the restrictions evolving from the Engle or the Weiss models are valid in practice. Time series of stock market indexes for some major stock exchanges yield empirical examples. In most cases, the statistical approximation to actual dynamic behaviour is improved substantially by considering augmented ARCH structures
Volume (Year): 7 (1997)
Issue (Month): 6 ()
|Contact details of provider:|| Web page: http://www.tandfonline.com/RAFE20 |
|Order Information:||Web: http://www.tandfonline.com/pricing/journal/RAFE20|
When requesting a correction, please mention this item's handle: RePEc:taf:apfiec:v:7:y:1997:i:6:p:575-586. 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: (Michael McNulty)
If references are entirely missing, you can add them using this form.