Bayesian analysis of periodic asymmetric power GARCH models.

Autor:	Aknouche, Abdelhakim, Demmouche, Nacer, Dimitrakopoulos, Stefanos, Touche, Nassim
Předmět:	BAYESIAN analysis GARCH model MARGINAL distributions ALGORITHMS MARKOV chain Monte Carlo
Zdroj:	Studies in Nonlinear Dynamics & Econometrics; Sep2020, Vol. 24 Issue 4, p1-24, 24p
Abstrakt:	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. [ABSTRACT FROM AUTHOR]
Databáze:	Complementary Index
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