price volatility, and compares their forecasting performance to the standard GARCH, fractionally integrated GARCH (FIGARCH …) and the two-state Markov-switching GARCH (MS-GARCH) models via three loss functions (the mean squared error, the mean … criteria and forecast horizons, while MS-GARCH mostly comes out as the least successful model. Applying various VaR backtesting …

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 …

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

(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 …