Multi-Period Forecasts of Volatility: Direct, Iterated, and Mixed-Data Approaches

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Modelling and Forecasting Multivariate Realized Volatility (2011, methodology): “This assumption is theoretically appealing, empirically reasonable at the daily frequency, and not uncommon in the literature; see, for example, Ghysels et al. (2009), who address the issue of longer-horizon volatility forecasting based on daily data.”
Harnessing jump component for crude oil volatility forecasting in the presence of extreme shocks (2019, methodology): “The MIDAS model is an econometric regression method developed to be applied in the context of mixed frequency data, which is an ideal analytical tool for the high frequency data based RV forecasting,(Ghysels et al., 2006, 2009).”
Risk Everywhere: Modeling and Managing Volatility (2018, unknown): “This issue also has been extensively studied in the MIDAS literature (see, e.g., Ghysels et al. 2009).”
A Bias and Variance Analysis for Multistep-Ahead Time Series Forecasting (2016, unknown): “For example, [32] considered three strategies: 1) recursive; 2) direct; and 3) mixed data sampling (MIDAS) for multistep volatility forecasting.”