Forecasting the conditional volatility of oil spot and futures prices with structural breaks and long memory models

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Volatility forecasting and risk management for commodity markets in the presence of asymmetry and long memory (2014, background): “…while past studies have extensively investigated the issue of volatility modeling and forecasting for crude oil and related petroleum products (Arouri et al., 2012b; Wei et al., 2010; and references therein), none of them have extended their samples to include natural gas, gold, and silver at…”
Forecasting oil price realized volatility using information channels from other asset classes (2017, methodology): “Similarly, several other authors model the conditional volatility of oil prices and forecast these volatilities, using univariate models such as the FIAPARCH, HYGARCH, EGARCH, FIGARCH, APARCH, as well as multivariate models such as the BEKK, VAR and Risk Metrics (see, Agnolucci, 2009; Wei et al., 20”
Interpreting the crude oil price movements: Evidence from the Markov regime switching model (2015, methodology): “[10] The GARCH-class models There are five models with parameter instability out of nine GARCH-based conditional volatility processes for oil prices, and a FIGARCH model seems to better fit the data Chang et al.”
Applied econometrics and implications for energy economics research (2015, background): “In a related study, Arouri et al. (2012) examine howmultiple structural breaks affect forecasts of oil spot and futures prices.”