Forecasting petroleum futures markets volatility: The role of regimes and market conditions

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Forecasting the realized volatility of the oil futures market: A regime switching approach (2017, methodology): “…oil price volatility are investigated based on the framework of the GARCH-class models (e.g., Agnolucci, 2009; Cheong, 2009; Kang et al., 2009; Mohammadi and Su, 2010; Wei et al., 2010; Nomikos and Pouliasis, 2011; Nomikos and Andriosopoulos, 2012; Wang and Wu, 2012; Efimova and Serletis, 2014).”
Oil Prices and Stock Markets: A Review of the Theory and Empirical Evidence (2018, background): “(2014), Nomikos and Pouliasis (2011), Kang et al .”
Forecasting oil price realized volatility using information channels from other asset classes (2017, methodology): “Nomikos and Pouliasis (2011) use Mix-GARCH and MRS-GARCH models to forecast the 1-day-ahead oil price volatility and find that both models are able to outperform the forecasts of the simple GARCH model.”
Energy markets volatility modelling using GARCH (2014, methodology): “Several studies compare ‘classic’multivariate GARCH specifications, such as the BEKK and CCC, with new multivariate models, including nonparametric GARCH [see Sadorsky (2006)] and Markov regime-switching GARCH [see Nomikos and Pouliasis (2011)], to name a few.”