Stability of nonlinear AR-GARCH models

Mika Meitz () and Pentti Saikkonen

No 632, SSE/EFI Working Paper Series in Economics and Finance from Stockholm School of Economics

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: - (search for similar items in EconPapers)
JEL-codes: C22 (search for similar items in EconPapers)
Pages: 23 pages
Date: 2006-06-01
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ict
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Citations: View citations in EconPapers (4)

Published in Journal of Time Series Analysis, 2008, pages 453-475.

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Related works:
Journal Article: Stability of nonlinear AR‐GARCH models (2008)
Working Paper: Stability of nonlinear AR-GARCH models (2007)
Working Paper: Stability of nonlinear AR-GARCH models (2006)
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Persistent link: https://EconPapers.repec.org/RePEc:hhs:hastef:0632

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