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Studies in Nonlinear Dynamics & Econometrics

Ed. by Mizrach, Bruce

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Volume 24 (2020)

Bayesian analysis of periodic asymmetric power GARCH models

Abdelhakim Aknouche
  • Corresponding author
  • Faculty of Mathematics, University of Science and Technology Houari, Boumediene, Algeria
  • College of Science, Qassim University, Buraidah, Saudi Arabia
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  • Other articles by this author:
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/ Nacer Demmouche / Stefanos Dimitrakopoulos / Nassim Touche
Published Online: 2019-10-19 | DOI: https://doi.org/10.1515/snde-2018-0112


In this paper, we set up a generalized periodic asymmetric power GARCH (PAP-GARCH) model whose coefficients, power, and innovation distribution are periodic over time. We first study its properties, such as periodic ergodicity, finiteness of moments and tail behavior of the marginal distributions. Then, we develop an MCMC algorithm, based on the Griddy-Gibbs sampler, under various distributions of the innovation term (Gaussian, Student-t, mixed Gaussian-Student-t). To assess our estimation method we conduct volatility and Value-at-Risk forecasting. Our model is compared against other competing models via the Deviance Information Criterion (DIC). The proposed methodology is applied to simulated and real data.

This article offers supplementary material which is provided at the end of the article.

Keywords: Bayesian forecasting; Deviance Information Criterion; Griddy-Gibbs; periodic asymmetric power GARCH model; probability properties; Value at Risk

MSC 2010: AMS 2000 Primary 62M10; Secondary 60F99


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Published Online: 2019-10-19

Citation Information: Studies in Nonlinear Dynamics & Econometrics, 20180112, ISSN (Online) 1558-3708, DOI: https://doi.org/10.1515/snde-2018-0112.

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