Stability of nonlinear AR-GARCH models

Mika Meitz () and Pentti Saikkonen

No 2006078, LIDAM Discussion Papers CORE from Université catholique de Louvain, Center for Operations Research and Econometrics (CORE)

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 B-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.

Date: 2006-09
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Citations: View citations in EconPapers (4)

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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:cor:louvco:2006078

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