Value-at-Risk estimation of energy commodities: A long-memory GARCH–EVT approach

A Stochastic Factor Model for Risk Management of Commodity Derivatives

“…Therefore, risk management of energy products prices becomes very important for both academicians and market participants, and many risk measurement tools have been proposed in the literature. A non-exhausted list includes: Cabedo and Moya (2003), Costello, Asem and Gardner (2008), Krehbiel and Adkins (2005), Marimoutou, Raggad and Trabelsi (2009), Kang and Yoon (2013), Youssef, Belkacem, and Mokni (2015), and Fiano and Grossi (2015). These papers employ a widely-used risk measure, Value-at-Risk (VaR) originally proposed by J.P. Morgan in 1994 (see Duffie and Pan, 1997, for a discussion of this measure), but differ in the model assumptions.…”

WTI crude oil option implied VaR and CVaR: An empirical application

“…Although the performance of many different statistically based historical risk models for the oil market has been deeply analyzed (e.g., Aloui & Mabrouk, 2010;Cabedo & Moya, 2003;Costello, Asem, & Gardner, 2008;Fan, Wei, & Xu, 2004;Fan, Zhang, Tsai, & Wei, 2008;Feng, Wu, & Jiang, 2004;Giot & Laurent, 2003;González-Pedraz, Moreno, & Peña, 2014;Lux, Segnon, & Gupta, 2016;Sadorsky, 2006;Youssef, Belkacem, & Mokni, 2015;and 1 It is well known that a time window that is too long makes today's value almost unconditional, and hence of almost no value, whereas one that is too short leads to statistically poor results and might leave out important past data. 2 It is worth noting that the square root extension is theoretically acceptable only in the presence of normal results.…”

Panel quantile regressions for estimating and predicting the value‐at‐risk of commodities

“…Second, the historical simulations used by, for example, Cabedo and Moya () have the opposite problem: They capture the empirical returns distribution but do not make it conditional on volatility. Third, more advanced parametric models mostly built within the family of generalized autoregressive conditional heteroskedasticity (GARCH) models improve fits (Aloui & Mabrouk, ; Chiu, Chuang, & Lai, ; Giot & Laurent, ; Hung, Lee, & Liu, ; Lux, Segnon, & Gupta, ; Youssef, Belkacem, & Mokni, ); however, they require fat‐tailed distributions, long memory, and other features that lead to heavy parameterization, making the approach less tractable.…”