Forecasting crude oil price volatility and value-at-risk: Evidence from historical and recent data

Abstract

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Geopolitical risks and the predictability of regional oil returns and volatility

“…Naturally, accurate prediction of oil market movements is of importance to academics, investors and policymakers alike. Understandably, there exists a large literature (see Baumeister, ; Lux et al , ; Degiannakis and Filis, ,; Gupta and Wohar, for detailed reviews) aiming to predict oil price movements using various types of econometric methodologies (univariate and multivariate; linear and non‐linear), and predictors (macroeconomic, financial, behavioural, institutional).…”

“…Understandably, there exists a large JEL classification: C22, C32, Q41. literature (see Baumeister, 2014;Lux et al, 2016;Filis, 2017a,2017b;Gupta and Wohar, 2017 for detailed reviews) aiming to predict oil price movements using various types of econometric methodologies (univariate and multivariate; linear and non-linear), and predictors (macroeconomic, financial, behavioural, institutional).…”

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.…”

“…Many researchers, therefore, study VaR using parametric approaches, in which the volatility in Equation (1) comes from a variety of GARCH models. Recent attempts include Youssef et al (2015), who combine long-memory GARCH models with extreme value theory; Lux et al (2016), in which Markov-switching multifractal and various GARCH models are applied to model and forecast oil price volatility; Chkili, Hammoudeh, and Nguyen (2014), in which a wide range of linear and nonlinear GARCH models are used to study the VaR of energy and precious metal commodities; and Giot and Laurent (2003), in which it is shown that skewed Student APARCH works best in forecasting VaR in commodity markets.…”