Forecasting daily exchange rate volatility using intraday returns

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Forecasting Daily Variability of the S&P 100 Stock Index Using Historical, Realised and Implied Volatility Measurements (2005, unknown): “For other volatility models, similar extensions have been considered, see Martens (2001) and Blair, Poon, and Taylor (2001), amongst others.”
Value-at-Risk Prediction: A Comparison of Alternative Strategies (2005, methodology): “…data, daily realized vola- tility can be ‘‘observed’’ (i.e., independent of a model and essentially error free) and then used for daily prediction purposes [see Martens (2001), Giot and Laurent (2004), Galbraith and Kisinbay (2005), Koopman, Jungbacker, and Hol (2005), and the references therein].”
Forecasting Volatility Using High-Frequency Data (2011, methodology): “GARCHX models of this type have been estimated by Blair et al. (2004), Martens (2001, 2002), Engle (2002), Barndorff-Nielsen and Shephard (2007), Barndorff-Nielsen et al. (2010), amongst others.”
Realized Volatility: A Review (2008, background): “Martens, M., van Dijk, D., de Pooter, M. (2004).”
Modelling daily value-at-risk using realized volatility and arch type models (2001, unknown): “Because 1See also Martens (2001) for a comparative study of daily vs. realized volatility in a volatility forecasting framework, albeit the author does not focus on the density distributions of the returns. the exchange is closed from 5 pm to 10 am of the next day, the first intraday return…”