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

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  • Arash Nademi
  • Rahman Farnoosh

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.

Suggested Citation

  • Arash Nademi & Rahman Farnoosh, 2014. "Mixtures of autoregressive-autoregressive conditionally heteroscedastic models: semi-parametric approach," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(2), pages 275-293, February.
    • Handle: RePEc:taf:japsta:v:41:y:2014:i:2:p:275-293
    DOI: 10.1080/02664763.2013.839129
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    1. Nademi, Arash & Nademi, Younes, 2018. "Forecasting crude oil prices by a semiparametric Markov switching model: OPEC, WTI, and Brent cases," Energy Economics, Elsevier, vol. 74(C), pages 757-766.

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