@article {LOPES2013262,
	title = {Seasonal FIEGARCH processes},
	journal = {Computational Statistics \& Data Analysis},
	volume = {68},
	year = {2013},
	pages = {262 - 295},
	abstract = {<p>Here we develop the theory of seasonal FIEGARCH processes, denoted by SFIEGARCH, establishing conditions for the existence, the invertibility, the stationarity and the ergodicity of these processes. We analyze their asymptotic dependence structure by means of the autocovariance and autocorrelation functions. We also present some properties regarding their spectral representation. All properties are illustrated through graphical examples and an application of SFIEGARCH models to describe the volatility of the S\&amp;P500 US stock index log-return time series in the period from December 13, 2004 to October 10, 2009 is provided.</p>
},
	keywords = {FIEGARCH process, Long-range dependence, Periodicity, Volatility},
	issn = {0167-9473},
	doi = {https://doi.org/10.1016/j.csda.2013.07.001},
	url = {http://www.sciencedirect.com/science/article/pii/S0167947313002466},
	author = {Lopes, S{\'\i}lvia R.C. and Prass, Taiane S.}
}
