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 …

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

threshold cointegration and CGARCH errors framework. The empirical contribution of our paper specifies the cointegrating …

these series. But from among the conditional heteroscedasticity models, the ARFIMA-FIGARCH model was selected as the best …

(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 …

. Accordingly, linear GARCH, fractionally integrated FI-GARCH, asymmetric power APGARCH and fractionally integrated asymmetric power … LSTAR-GARCH are extended to their fractionally integrated asymmetric power versions and STAR-ST-FIGARCH and STAR … are as follows: LSTAR-LST-GARCH-MLP, LSTAR-LST-FIGARCH-MLP, LSTAR-LST-APGARCH-MLP and LSTAR-LST-FIAPGARCH-MLP. Empirical …