Modeling and predicting the CBOE market volatility index

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COVID-19 and Stock Market Volatility (2020, unknown): “The expected market volatility is approximated by pooling the information from option prices over the whole volatility skew, with weights that are inversely proportional to the squared strike prices (Fernandes et al. (2014)).”
Forecasting oil price realized volatility using information channels from other asset classes (2017, methodology): “Andersen et al. (2007), Corsi (2009), Busch et al. (2011) and Fernandes et al. (2014) also argue that HAR models are more successful in forecasting asset price volatility as they are parsimonious and they capture the longmemory that is observed in asset price volatility. time-series, the authors…”
Forecasting global stock market implied volatility indices (2018, background): “…autoregressive models (HAR) are more successful in forecasting volatility due to the fact that they are parsimonious and they can capture the longmemory that is observed in volatility (see, inter alia, Andersen et al., 2007; Corsi, 2009; Busch et al., 2011; Fernandes et al., 2014, Sevi, 2014).”
Regime Switching Vine Copula Models for Global Equity and Volatility Indices (2016, background): “Finally, all these potential applications justify research on the modeling and prediction of volatility indices themselves as well (cf. Dotsis et al. (2007) or Fernandes et al. (2014)).”