An Overview of FIGARCH and Related Time Series Models


  • Maryam Tayefi Department of Statistics and Centre for Advanced Studies University of Pune, India
  • T.V. Ramanathan Department of Statistics and Centre for Advanced Studies University of Pune, India



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 FIGARCH models allows to be a better candidate than other conditional heteroscedastic models for modeling volatility in exchange rates, option prices, stock market returns and inflation rates. We discuss some of the important properties of FIGARCH models in
this review. We also compare the FIGARCH with the autoregressive fractionally integrated moving average (ARFIMA) model. Problems related to parameter estimation and forecasting using a FIGARCH model are presented. The application of a FIGARCH model to exchange rate data is discussed. We briefly introduce some other models, that are closely related to FIGARCH models. The paper ends with some concluding remarks and future directions of research.


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How to Cite

Tayefi, M., & Ramanathan, T. (2016). An Overview of FIGARCH and Related Time Series Models. Austrian Journal of Statistics, 41(3), 175–196.