SSE/EFI Working Paper Series in Economics and Finance
No 632:
Stability of nonlinear AR-GARCH models
Mika Meitz ()
and Pentti Saikkonen ()
Abstract: This paper studies the stability of nonlinear
autoregressive models with conditionally heteroskedastic errors. We
consider a nonlinear autoregression of order p (AR(p)) with the conditional
variance specified as a nonlinear first order generalized autoregressive
conditional heteroskedasticity (GARCH(1,1)) model. Conditions under which
the model is stable in the sense that its Markov chain representation is
geometrically ergodic are provided. This implies the existence of an
initial distribution such that the process is strictly stationary and
beta-mixing. Conditions under which the stationary distribution has finite
moments are also given. The results cover several nonlinear specifications
recently proposed for both the conditional mean and conditional
variance.
Keywords: -; (follow links to similar papers)
JEL-Codes: C22; (follow links to similar papers)
23 pages, June 1, 2006
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- This paper is published as:
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Meitz, Mika and Pentti Saikkonen, (2008), 'Stability of nonlinear AR-GARCH models', Journal of Time Series Analysis, Vol. 29, pages 453-475
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