Estimation of value-at-risk for energy commodities via fat-tailed GARCH models

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Volatility forecasting and risk management for commodity markets in the presence of asymmetry and long memory (2014, background): “…empirical stylized characteristics such as non-normal distribution, asymmetry, structural breaks and fat tails that affect model performance (e.g., Aloui and Mabrouk, 2010; Cheng and Hung, 2011; Cheong, 2009; Hung et al., 2008), and thereby require experimentation with different volatility models.”
Extreme risk spillovers between crude oil and stock markets (2015, methodology): “In order to estimate VaR, the GARCH model is one of the most commonly used methods that can capture the stylized facts of financial returns such as volatility clustering, fat tails, skewness, and leverage effects (e.g., Fan et al., 2008; Hung et al., 2008).”