<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Prass, Taiane S.</style></author><author><style face="normal" font="default" size="100%">Lopes, Sílvia R.C.</style></author><author><style face="normal" font="default" size="100%">Jorge A. Achcar</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">MCMC Bayesian Estimation in FIEGARCH Models</style></title><secondary-title><style face="normal" font="default" size="100%">Communications in Statistics - Simulation and Computation</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://doi.org/10.1080/03610918.2014.932800</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">9</style></number><publisher><style face="normal" font="default" size="100%">Taylor &amp; Francis</style></publisher><volume><style face="normal" font="default" size="100%">45</style></volume><pages><style face="normal" font="default" size="100%">3238-3258</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;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 &amp;gt; 2) and heavier (ν &amp;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;amp;P500 U.S. stock market index is provided.&lt;/p&gt;
</style></abstract></record></records></xml>