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 …

(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 …

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

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 …

Moving Average (ARFIMA) models in predicting stock returns. We present evidence which suggests that ARFIMA models estimated … using a variety of estimation procedures yield better forecasting results than the non-ARFIMA (AR, MA, ARMA and GARCH …

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 …