Accounting for conditional leptokurtosis and closing days effects in FIGARCH models of daily exchange rates

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Estimating stock market volatility using asymmetric GARCH models (2008, methodology): “The Generalized ARCH (GARCH) model of Bollerslev (1986) fulfills this requirement as it is based on an infinite ARCH specification which reduces the number of estimated parameters from infinity to two. Both the ARCH and GARCH models capture volatility clustering and leptokurtosis, but as their distr”
G@RCH 2.2: An Ox Package for Estimating and Forecasting Various ARCH Models (2001, methodology): “0 package with a concrete application, we analyze the daily German mark/British pound exchange rate data of Bollerslev and Ghysels (1996). The dataset consists in 1974 returns covering the period from January, 3, 1984 through December, 31, 1991.”
Time-varying correlation between oil and stock market volatilities: Evidence from oil-importing and oil-exporting countries (2016, methodology): “The reason of using Student t distribution is that GARCH models often have an issue in capturing the thick tails property of financial time series and it is recommended to apply non-normal distributions to better model this excess kurtosis (Bollerslev, 1987; Kaiser, 1996; Beine et al., 2000, and Pet”
Central Bank intervention and foreign exchange rates: new evidence from FIGARCH estimations (2002, methodology): “In this respect, following Beine et al. (2002) among others, we compare the results obtained with the Normal and with the Student’s-t distributions.”