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 …

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 …

(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 …