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 …

El modelo gaussiano GARCH(1,1) ha sido empleado, tradicionalmente, en el estudio de la tasa de cambio; sin embargo, un … número importante de estudios recientes (utilizando modelos FIGARCH e HYGARCH) ha encontrado evidencia de persistencia en su …

GARCH model augmented by including an additional explanatory variable - the so-called GARCH-X model. The additional … to the results of Jensen and Rahbek (2004, Econometric Theory 20) who develop similar results for the pure GARCH model …

variants of the widely used GARCH family of second order persistence models that control for these effects, and compare the …

fractionally-integrated GARCH for the conditional variance. The interaction between the funds is modelled as the Dynamic …