Many previous analyses of inflation have used either long memory or nonlinear time series models. This paper suggests a … simple adaptive modification of the basic ARFIMA model, which uses a flexible Fourier form to allow for a time varying … also that the new model can be efficiently estimated by a QMLE approach. We investigate monthly CPI inflation series for …

This paper introduces a new long memory volatility process, denoted by Adaptive FIGARCH, or A-FIGARCH, which is …. A Monte Carlo study ?nds that the A-FIGARCH model outperforms the standard FIGARCH model when structural change is …

in the returns and volatility might be modeled by using the ARFIMA-FIGARCH model. The results of the ARFIMAFIGARCH model …

(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 …

In this paper we apply compactly supported wavelets to the ARFIMA(p,d,q) long-memory process to develop an alternative … of compactly supported wavelets, series length, and contamination by generating ARFIMA(p,d,q) processes for different …. In our simulations we find the wavelet MLE to be superior to the approximate MLE when estimating contaminated ARFIMA(0,d …

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 …