Stability of nonlinear AR-GARCH models

Working paper

This paper studies the stability of nonlinear autoregressive models with conditionality 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 distribut...

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Authors: Meitz, M; Saikkonen, P

• Publication status: Published
• Publisher: University of Oxford Publisher's website
• Series: Department of Economics Discussion Paper Series
• Publication date: 2007-05-01
• Paper number: 328
• Keywords: nonlinear autoregression; strict stationarity; Markov models; geometric ergodicity; existence of moments; nonlinear time series models; mixing; generalized autoregressive conditional heteroskedasticity