Mixtures of autoregressive-autoregressive conditionally heteroscedastic models: semi-parametric approach

We propose data generating structures which can be represented as a mixture of autoregressive-autoregressive conditionally heteroscedastic models. The switching between the states is governed by a hidden Markov chain. We investigate semi-parametric estimators for estimating the functions based on the quasi-maximum likelihood approach and provide sufficient conditions for geometric ergodicity of the process. We also present an expectation-maximization algorithm for calculating the estimates numerically.