Aknouche, A, Demmouche, N, Dimitrakopoulos, S orcid.org/0000-0002-0043-180X et al. (1 more author) (2019) Bayesian analysis of periodic asymmetric power GARCH models. Studies in Nonlinear Dynamics & Econometrics. ISSN 1558-3708
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 Walter de Gruyter GmbH, Berlin/Boston. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | Bayesian forecasting; Deviance Information Criterion; Griddy-Gibbs; periodic asymmetric power GARCH model; probability properties; Value at Risk |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Business (Leeds) > Economics Division (LUBS) (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 20 Sep 2019 13:12 |
Last Modified: | 19 Oct 2020 00:40 |
Status: | Published online |
Publisher: | De Gruyter |
Identification Number: | 10.1515/snde-2018-0112 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:151140 |