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 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.

Authors: Meitz, M; Saikkonen, P

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