Bayesian analysis of periodic asymmetric power GARCH models

Aknouche Abdelhakim (), Demmouche Nacer, Dimitrakopoulos Stefanos and Touche Nassim
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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

Studies in Nonlinear Dynamics & Econometrics, 2020, vol. 24, issue 4, 24

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.

Keywords: Bayesian forecasting; Deviance Information Criterion; Griddy-Gibbs; periodic asymmetric power GARCH model; probability properties; Value at Risk (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1515/snde-2018-0112

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