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Bayesian analysis of periodic asymmetric power GARCH models

Author

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  • Aknouche Abdelhakim

    (Faculty of Mathematics, University of Science and Technology Houari, Boumediene, Algeria)

  • Demmouche Nacer

    (Department of Mathematics, University Akli Mohand Oulhadj, Bouira, Algeria)

  • Dimitrakopoulos Stefanos

    (Economics Division, Leeds University, Leeds, UK)

  • Touche Nassim

    (Faculty of Exact Sciences, University of Bejaia, Bejaia, Algeria)

Abstract

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.

Suggested Citation

  • Aknouche Abdelhakim & Demmouche Nacer & Dimitrakopoulos Stefanos & Touche Nassim, 2020. "Bayesian analysis of periodic asymmetric power GARCH models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 24(4), pages 1-24, September.
    • Handle: RePEc:bpj:sndecm:v:24:y:2020:i:4:p:24:n:5
    DOI: 10.1515/snde-2018-0112
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    Cited by:

    1. Fu, Jin-Yu & Lin, Jin-Guan & Hao, Hong-Xia, 2023. "Volatility analysis for the GARCH–Itô–Jumps model based on high-frequency and low-frequency financial data," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1698-1712.

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