A generalized mixture integer-valued GARCH model

Huiyu Mao, Fukang Zhu and Yan Cui ()
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Huiyu Mao: Jilin University
Fukang Zhu: Jilin University
Yan Cui: Jilin University

Statistical Methods & Applications, 2020, vol. 29, issue 3, No 5, 527-552

Abstract: Abstract We propose a generalized mixture integer-valued generalized autoregressive conditional heteroscedastic model to provide a more flexible modeling framework. This model includes many mixture integer-valued models with different distributions already studied in the literature. The conditional and unconditional moments are discussed and the necessary and sufficient first- and second-order stationary conditions are derived. We also investigate the theoretical properties such as strict stationarity and ergodicity for the mixture process. The conditional maximum likelihood estimators via the EM algorithm are derived and the performances of the estimators are studied via simulation. The model can be selected in terms of both the number of mixture regimes and the number of orders in each regime by several different criteria. A real-life data example is also given to assess the performance of the model.

Keywords: Ergodicity; Integer-valued time series; Mixture model; Stationarity (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s10260-019-00498-2

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