financial time series and use it to improve the short-term forecasts from GARCH models. We study different generalizations of … GARCH that allow for several time scales. On our holding sample, none of the considered models can fully exploit the …

the intraday volatility using a FIGARCH model and the intraday seasonality by the Fourier Flexible Form. We find that …

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 …

long memory. This paper uses fractionally integrated GARCH (FIGARCH) to test and account for long memory. The analysis …

(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 … regression technique was used for estimation of different ARFIMA models. Furthermore, different GARCH-type models were also …

memory in squared residuals in FIGARCH models. The investigations are performed by means of simulations FIGARCH(0, d, 0) and … FIGARCH(1, d, 1) models for selected parameters. Simulation results suggest, that estimates of the conditional variance long … between the long memory estimates of squared residuals and the fractional integration parameter d of FIGARCH model can be …

regression function and a fractionally integrated skedastic function.We estimate ARFIMA-GARCH and ARFIMA-FIGARCH models under two …