Structural Change and Long Range Dependence in Volatility of Exchange Rates: Either, Neither or Both?

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Modelling the volatility of the Dow Jones Islamic Market World Index using a fractionally integrated time-varying GARCH (FITVGARCH) model (2014, background): “…structural breaks), a body of research has suggested that both long memory and structural changes simultaneously characterize the structure of financial returns volatility (Lobato and Savin, 1998; Beine and Laurent, 2000; Morana and Beltratti, 2004; Martens et al., 2004; Baillie and Morana, 2009).”
Testing for Structural Breaks in Variance with Additive Outliers and Measurement Errors (2006, methodology): “In spite of being based on strong restrictions, such as normally-distributed data, this method has been intensively used on financial time-series; see, among others, Aggarwal et al., (1999), Morana and Beltratti (2004), Nouira et al. (2004), and more recently Hyung, Poon and Granger (2005).”
Modeling and forecasting stock return volatility using a random level shift model (2010, background): “Morana and Beltratti (2004) also argue that breaks in”
Modeling Long Memory and Structural Breaks in Conditional Variances: an Adaptive FIGARCH Approach (2009, background): “See for instance the discussion in Diebold and Inoue (2001) and the empirical results in Morana and Beltratti (2004).”