Mixture Periodic GARCH Models: Theory and Applications

Saïd Souam () and Faycal Hamdi

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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.

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Date: 2018
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Citations: View citations in EconPapers (2)

Published in Empirical Economics, 2018, 55, pp.1925-1956

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-01589209

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