Stability of nonlinear AR‐GARCH models

Mika Meitz () and Pentti Saikkonen

Journal of Time Series Analysis, 2008, vol. 29, issue 3, 453-475

Abstract: Abstract. This article 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 β‐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, and only require mild moment conditions.

Date: 2008
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Citations: View citations in EconPapers (17)

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https://doi.org/10.1111/j.1467-9892.2007.00562.x

Related works:
Working Paper: Stability of nonlinear AR-GARCH models (2007)
Working Paper: Stability of nonlinear AR-GARCH models (2006)
Working Paper: Stability of nonlinear AR-GARCH models (2006)
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Persistent link: https://EconPapers.repec.org/RePEc:bla:jtsera:v:29:y:2008:i:3:p:453-475

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