(ARFIMA) and fractionally integrated generalized autoregressive conditional heteroskedasticity (FIGARCH) models, mainly for … feature in FIGARCH models makes them a better candidate than other conditional heteroskedasticity models for modeling … volatility in financial series. ARFIMA model also has a considerable capacity for modeling the return behavior of these time …

Although the properties of the ARCH(∞) model are well investigated, the existence of long memory FIGARCH and IARCH … finite variance FIGARCH and IARCH models and, thus, the possibility of long memory in the ARCH setting was doubtful. The … present paper solves this controversy by showing that FIGARCH and IARCH equations have a non-trivial covariance stationary …

Although the properties of the ARCH(∞) model are well investigated, the existence of long memory FIGARCH and IARCH … finite variance FIGARCH and IARCH models and, thus, the possibility of long memory in the ARCH setting was doubtful. The … present paper solves this controversy by showing that FIGARCH and IARCH equations have a non-trivial covariance stationary …

choice. This paper examines how the choice between modelling stationary time series as ARMA or ARFIMA processes affects the … accuracy of forecasts. This is done, for first-order autoregressions and moving averages and for ARFIMA 1,d,0) processes, by …

by means of fractionally ARIMA (ARFIMA) models, and show that the three time series can be specified in terms of this … type of models with orders of integration higher than one but smaller than two. Comparing the ARFIMA specifications with …