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Mixture Periodic GARCH Models: Theory and Applications

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  • Saïd Souam

    (EconomiX - EconomiX - UPN - Université Paris Nanterre - CNRS - Centre National de la Recherche Scientifique)

  • Faycal Hamdi

Abstract

This paper discusses mixture periodic GARCH (M-PGARCH) models that constitute very flexible class of nonlinear time series models of the conditional variance. It turns out that they are more parsimonious comparatively to high-order MPARCH models. We first provide some probabilistic properties of this class of models. We thus propose an estimation method based on the Expectation-Maximization (EM) algorithm. Finally, we apply this methodology to model the spot rates of the Algerian dinar against euro and U.S. dollar. This empirical analysis shows that M-PGARCH models yield the best performance among the competing models.

Suggested Citation

  • Saïd Souam & Faycal Hamdi, 2018. "Mixture Periodic GARCH Models: Theory and Applications," Post-Print hal-01589209, HAL.
    • Handle: RePEc:hal:journl:hal-01589209
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    Cited by:

    1. Yang Zhang & Yidong Peng & Xiuli Qu & Jing Shi & Ergin Erdem, 2021. "A Finite Mixture GARCH Approach with EM Algorithm for Energy Forecasting Applications," Energies, MDPI, vol. 14(9), pages 1-22, April.
    2. Bibi, Abdelouahab & Ghezal, Ahmed, 2017. "Asymptotic properties of QMLE for periodic asymmetric strong and semi-strong GARCH models," MPRA Paper 81126, University Library of Munich, Germany.

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