MCMC Bayesian Estimation in FIEGARCH Models
Abstract
Bayesian inference for fractionally integrated exponential generalized autoregressive conditional heteroskedastic (FIEGARCH) models using Markov Chain Monte Carlo (MCMC) methods is described. A simulation study is presented to access the performance of the procedure, under the presence of long-memory in the volatility. Samples from FIEGARCH processes are obtained upon considering the generalized error distribution (GED) for the innovation process. Different values for the tail-thickness parameter \nu are considered covering both scenarios, innovation processes with lighter (\nu<2) and heavier (\nu>2) tails than the Gaussian distribution (\nu=2). A sensitivity analysis is performed by considering different prior density functions and by integrating (or not) the knowledge on the true parameter values to select the hyperparameter values.
Cite
@article{arxiv.1304.1733,
title = {MCMC Bayesian Estimation in FIEGARCH Models},
author = {Taiane S. Prass and Sílvia R. C. Lopes and Jorge A. Achcar},
journal= {arXiv preprint arXiv:1304.1733},
year = {2013}
}