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

Arash Nademi and Rahman Farnoosh

Journal of Applied Statistics, 2014, vol. 41, issue 2, 275-293

Abstract: 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.

Date: 2014
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DOI: 10.1080/02664763.2013.839129

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