Data for: Modeling energy price dynamics: GARCH versus stochastic volatility

Abstract of associated article: We compare a number of GARCH and stochastic volatility (SV) models using nine series of oil, petroleum product and natural gas prices in a formal Bayesian model comparison exercise. The competing models include the standard models of GARCH(1,1) and SV with an AR(1) log-volatility process, as well as more flexible models with jumps, volatility in mean, leverage effects, and t distributed and moving average innovations. We find that: (1) SV models generally compare favorably to their GARCH counterparts; (2) the jump component and t distributed innovations substantially improve the performance of the standard GARCH, but are unimportant for the SV model; (3) the volatility feedback channel seems to be superfluous; (4) the moving average component markedly improves the fit of both GARCH and SV models; and (5) the leverage effect is important for modeling crude oil prices—West Texas Intermediate and Brent—but not for other energy prices. Overall, the SV model with moving average innovations is the best model for all nine series.

关联论文摘要：我们基于原油、成品油及天然气共9组价格序列，通过正式的贝叶斯模型比较实验，对多款广义自回归条件异方差（GARCH）与随机波动率（SV）模型展开对比分析。本次对比的模型涵盖标准GARCH(1,1)与带AR(1)对数波动率过程的SV模型，同时也包含更具灵活性的拓展模型，例如引入跳跃项、波动率均值效应、杠杆效应，以及服从t分布与移动平均的扰动项的模型。我们得到如下结论：(1) 随机波动率模型的表现普遍优于对应广义自回归条件异方差模型；(2) 跳跃分量与t分布扰动项可显著提升标准GARCH模型的拟合性能，但对SV模型而言并无显著增益；(3) 波动率反馈渠道似乎并无必要；(4) 移动平均分量可显著提升GARCH与SV两类模型的拟合效果；(5) 杠杆效应对原油价格——西德克萨斯中质原油（West Texas Intermediate）与布伦特原油（Brent）——的建模至关重要，但对其他能源价格而言则无关紧要。综合来看，带移动平均扰动项的SV模型是适配全部9组价格序列的最优模型。