Do High-Frequency Measures of Volatility Improve Forecasts of Return Distributions?

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Realizing smiles: Options pricing with realized volatility (2013, unknown): “We thus estimate the continuous component of the RV through the following three steps: (i) estimate the total variation of the log-price process using the tick-by-tick Two-Scales (TS) estimator proposed by Zhang et al. (2005) (with a fast scale of two ticks and a slower one of 20 ticks); (ii) purify”
Modeling Realized Covariances and Returns (2013, methodology): “The approach to modelling components is related to Andersen, Bollerslev and Diebold (2007), Corsi (2009), Maheu and McCurdy (2011) among others which uses the Heterogeneous AutoRegressive (HAR) model of realized variance in the univariate case in order to capture long-memory like features of…”
Semiparametric Conditional Quantile Models for Financial Returns and Realized Volatility (2013, background): “…for realized volatility with either parametric or nonparametric estimators of conditional distributions, and the recent contributions by Brownlees & Gallo (2009), Shephard & Sheppard (2009) and Maheu & McCurdy (2010), who base their predictive densities on parametric return-based volatility models.”
High-Dimensional Copula-Based Distributions with Mixed Frequency Data (2016, unknown): “~ uit = Zituit + (1 Zit) (1 uit) , t = 1; 2; :::T (27) where [u1t; :::; uNt] ut s iid C ( ) and Zit s iid Bernoulli (1=2) , and Zit ? Zjt 8 i 6= j”