SSE/EFI Working Paper Series in Economics and Finance
Properties of the Autocorrelation Function of Squared Observations for Second Order GARCH Processes under Two Sets of Parameter Constraints
() and Timo Teräsvirta
Abstract: Nonnegativety constraints on the parameters of the GARCH
(p, Q) model may be relaxed without giving up the requirement of the
conditional variance remaining non- negative with probability one. This
paper looks into the consequences of adopting these less severe constraints
in the GARCH (2,2) case and its two second-order special cases, GARCH (2,1)
and GARCH (1,2). This is done by comparing the autocorrelation function of
squared observations under these two sets of constraints. The less severe
constraints allow more flexibility in the shape of the autocorrelation
function than the constraints restricting the parameters to be nonnegative.
The theory is illustrated by an empirical example.
Keywords: Autoregressive conditional heteroskedasticity; conditional variance; fourth moment condition; time series; volatility; (follow links to similar papers)
JEL-Codes: C22; (follow links to similar papers)
18 pages, April 1997
- This paper is published as:
He, Changli and Timo Teräsvirta, (1999), 'Properties of the Autocorrelation Function of Squared Observations for Second Order GARCH Processes under Two Sets of Parameter Constraints', Journal of Time Series Analysis, Vol. 20, No. 1, pages 23-30
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