Time variation and persistence are crucial properties of volatility that are often studied separately in oil-based volatility forecasting models. Here, we propose a novel approach that allows shocks with heterogeneous persistence to vary smoothly over time, and thus model the two together. We argue that this is important because such dynamics arise naturally from the dynamic nature of shocks in oil-based commodities. We identify such dynamics from the data using localised regressions and build a model that significantly improves volatility forecasts. Such forecasting models, based on a rich persistence structure that varies smoothly over time, outperform state-of-the-art benchmark models and are particularly useful for forecasting over longer horizons.

翻译：时间变化性和持久性是波动率的关键属性，在基于石油类商品的波动率预测模型中通常被分开研究。本文提出了一种新方法，允许具有异质持久性的冲击随时间平滑变化，从而将两者联合建模。我们论证了这一点的重要性，因为此类动态特征天然源于石油类商品冲击的动态本质。我们通过局部回归从数据中识别此类动态，并构建了能够显著提升波动率预测精度的模型。这种基于丰富持久性结构且随时间平滑变化的预测模型，不仅优于最先进的基准模型，尤其适用于较长预测区间的波动率预测。