This paper reviews the theory and applications related to fractionally integrated generalized autoregressive conditional heteroscedastic (FIGARCH) models, mainly for describing the observed persistence in the volatility of a time series. The long memory nature of FIGARCHmodels allows to be a better candidate than other conditional heteroscedastic models for modeling volatility in exchange rates...

This paper introduces a new long memory volatility process, denoted by Adaptive FIGARCH, or A-FIGARCH, which is designed to account for both long memory and structural change in the conditional variance process. Structural change is modeled by allowing the intercept to follow the smooth ‡exible functional form due to Gallant (1984). A Monte Carlo study …nds that the A-FIGARCH model outperforms ...

An indirect estimator is proposed for two long memory volatility models; the fractionally integrated generalised autoregressive conditional heteroskedasticity (FIGARCH) model and the long memory stochastic volatility (LMSV) model. The small sample properties of the indirect estimator are compared to the small sample properties of conventional maximum likelihood estimators. It is found that the ...

This article compares the performance of bivariate error correction GARCH and FIGARCH models when estimating long term dynamic minimum variance hedge ratios (MVHRs) on the Australian All Ordinaries Index. The paper therefore introduces the bivariate error correction FIGARCH model into the hedging literature, which to date has only employed the GARCH class of processes. This is important for tho...

We consider the modelling of volatility on closely related markets. Univariate fractional Ž . volatility FIGARCH models are now standard, as are multivariate GARCH models. In this paper, we adopt a combination of the two methodologies. There is as yet little consensus on the methodology for testing for fractional cointegration. The contribution of this paper is to demonstrate the feasibility of...