fractionally integrated GARCH (FIGARCH) model. Monte Carlo methods are used to characterize the finite sample distributions of … these statistics when data are generated from GARCH(1,1), component GARCH and FIGARCH models. For several daily financial …This paper investigates if component GARCH models introduced by Engle and Lee(1999) and Ding and Granger(1996) can …

fractionally integrated GARCH (FIGARCH) model. Monte Carlo methods are used to characterize the finite sample distributions of … these statistics when data are generated from GARCH(1,1), component GARCH and FIGARCH models. For several daily financial …This paper investigates if component GARCH models introduced by Engle and Lee(1999) and Ding and Granger(1996) can …

log-squared returns. GARCH models extensively used in empirical analysis do not account for long memory in volatility. The … integrated generalized autoregressive conditional heteroscedasticity (FIGARCH) model. For the purpose, daily values of 38 indices … of long memory in volatility of all the index returns. This shows that FIGARCH model better describes the persistence in …

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 …

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 …