International evidence on crude oil price dynamics: Applications of ARIMA-GARCH models

By H. Mohammadi, Li-Ling Su. 2010

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Modeling energy price dynamics: GARCH versus stochastic volatility (2016, methodology): “…the conditional variance is a deterministic function of model parameters and past data (see, e.g., Fong and See, 2001; Sadorsky, 2006; Kang, Kang, and Yoon, 2009; Agnolucci, 2009; Mohammadi and Su, 2010; Nomikos and Andriosopoulos, 2012; Mason and Wilmot, 2014; Manera, Nicolini, and Vignati, 2014).”
The VEC-NAR model for short-term forecasting of oil prices (2019, background): “Mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) are commonly used to estimate the predictive performance of forecasting models (Cheong, 2009; Mohammadi and Su, 2010; Movagharnejad et al., 2011; Jammazi and Aloui, 2012; Suganthia and Samuel, 2012; Xu and Ouennich”
Forecasting: theory and practice (2020, background): “Extensive research on crude oil price forecasting has focused predominantly on the econometric models, such as VAR, ARCH-type, ARIMA, and Markov models (see, for example, Mirmirani and Li, 2004; Agnolucci, 2009; Mohammadi and Su, 2010; e Silva et al., 2010).”
Volatility forecasting and risk management for commodity markets in the presence of asymmetry and long memory (2014, methodology): “19 also reach similar conclusions (e.g., Choi and Hammoudeh, 2009; Cheong, 2009; Aloui and Mabrouk, 2010; Mohammadi and Su, 2010; Wei et al., 2010).”