  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.
