FIGARCH(1,d,1) model with three different distributions : Normal, Student-t, and skewed Student-t. The value-at-risk (VaR … test the FIGARCH(1,d,1) models with skewed Student-t distribution perform better than those of generated by normal …

This paper introduces a new long memory volatility process, denoted by Adaptive FIGARCH, or A-FIGARCH, which is …. A Monte Carlo study ?nds that the A-FIGARCH model outperforms the standard FIGARCH model when structural change is …

We consider the modelling of volatility on closely related markets. Univariate fractional volatility (FIGARCH) models … are now standard, as are multivariate GARCH models. In this paper we adopt a combination of the two methodologies. There … to demonstrate the feasibility of estimating and testing cointegrated bivariate FIGARCH models. We apply these methods to …

Daily futures returns on six important commodities are found to be well described as FIGARCH fractionally integrated …

This paper extends the Fractionally integrated GARCH (FIGARCH) model by incorporating Normal Inverse Gaussian … asymmetry and skewness in the distribution of financial returns. GARCH and FIGARCH models for daily log exchange rate returns … with Normal, Student's t and NIG error distributions as well as GARCH/FIGARCH-in-mean models with t errors are estimated …

models, nested in an ARMA(p,q)- FIGARCH(P,D,Q), to capture dependence of grain cash price volatility and compares the …-t density intended to account for fat-tailed properties of the data. We find suitability of the FIGARCH type models under the … student-t distribution and competitiveness of the parsimonious FIGARCH(1,d,0) for modeling long memory volatility. …

long memory. This paper uses fractionally integrated GARCH (FIGARCH) to test and account for long memory. The analysis …