We analyze the out‐of‐sample predictive power of sentiment for the realized volatility of agricultural commodity price returns. We use high‐frequency intra‐day data covering the period from 2009 to 2020 to estimate realized volatility. Our baseline forecasting model is a heterogeneous autoregressive (HAR) model, which we extend to include sentiment. We further enhance this model by incorporating various key realized moments such as leverage, realized skewness, realized kurtosis, realized upside (“good”) volatility, realized downside (“bad”) volatility, realized jumps, realized upside tail risk, and realized downside tail risk. In order to setup a forecasting model, we use (i) forward and backward stepwise predictor selection and (ii) a model‐based averaging algorithm. The forecasting models constructed through these algorithms outperform both the baseline HAR‐RV model and the HAR‐RV‐sentiment model. We conclude that, for the agricultural commodities studied in our research, realized moments play a more significant role in forecasting realized volatility compared to sentiment.

中文翻译：

---

预测农产品价格的实际波动：情绪重要吗？

我们分析了情绪对农产品价格回报实际波动性的样本外预测能力。我们使用 2009 年至 2020 年期间的高频日内数据来估计已实现的波动性。我们的基线预测模型是异构自回归 (HAR) 模型，我们将其扩展为包含情绪。我们通过结合各种关键的已实现时刻（例如杠杆、已实现的偏度、已实现的峰度、已实现的上行（“好”）波动率、已实现的下行（“坏”）波动率、已实现的跳跃、已实现的上行尾部风险和已实现的下行）来进一步增强该模型尾部风险。为了建立预测模型，我们使用（i）向前和向后逐步预测器选择和（ii）基于模型的平均算法。通过这些算法构建的预测模型优于基线 HAR-RV 模型和 HAR-RV 情感模型。我们的结论是，对于我们研究的农产品来说，与情绪相比，已实现时刻在预测已实现波动性方面发挥着更重要的作用。