@article {doi:10.1080/03610918.2014.932800,
	title = {MCMC Bayesian Estimation in FIEGARCH Models},
	journal = {Communications in Statistics - Simulation and Computation},
	volume = {45},
	number = {9},
	year = {2016},
	pages = {3238-3258},
	publisher = {Taylor \& Francis},
	abstract = {<p>Bayesian inference for fractionally integrated exponential generalized autoregressive conditional heteroscedastic (FIEGARCH) models using Markov chain Monte Carlo (MCMC) methods is described. A simulation study is presented to assess 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 ν are considered covering both scenarios, innovation processes with lighter (ν \&gt; 2) and heavier (ν \&lt; 2) tails than the Gaussian distribution (ν = 2). A comparison between the performance of quasi-maximum likelihood (QML) and MCMC procedures is also discussed. An application of the MCMC procedure to estimate the parameters of a FIEGARCH model for the daily log-returns of the S\&amp;P500 U.S. stock market index is provided.</p>
},
	doi = {10.1080/03610918.2014.932800},
	url = {https://doi.org/10.1080/03610918.2014.932800},
	author = {Prass, Taiane S. and Lopes, S{\'\i}lvia R.C. and Jorge A. Achcar}
}
