This study investigates the potential effects of environmental factors on fluctuations in agricultural commodity futures markets, by constructing a new category of daily exogenous predictors related to air pollution, weather, climate change, and investor attention. The empirical results from out-of-sample analyses suggest that the heterogeneous autoregressive (HAR) model incorporating all these exogenous predictors is more likely to outperform other HAR-type models. Additionally, economic evaluations demonstrate the superior performance of models incorporating investors' attention to climate change or extreme weather as predictors. While not all exogenous predictors are equally important for volatility forecasts, adopting appropriate variable selection methods to handle different sets of exogenous predictors can lead to better performance than the HAR benchmark. With the inclusion of air pollution or weather factors in the HAR model, a portfolio with an annualized average excess return of 16.2068% or a Sharpe ratio of 10.0431 can be achieved for the wheat futures, respectively.

中文翻译：

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空气污染、天气因素以及农产品期货的实际波动率预测

本研究通过构建与空气污染、天气、气候变化和投资者注意力相关的一类新的日常外生预测变量，调查环境因素对农产品期货市场波动的潜在影响。样本外分析的实证结果表明，包含所有这些外源预测变量的异质自回归 (HAR) 模型更有可能优于其他 HAR 类型模型。此外，经济评估表明，将投资者对气候变化或极端天气的关注作为预测因子的模型具有卓越的性能。虽然并非所有外生预测变量对于波动率预测都同样重要，但采用适当的变量选择方法来处理不同的外生预测变量集可以带来比 HAR 基准更好的性能。在HAR模型中纳入空气污染或天气因素后，小麦期货的年化平均超额收益和夏普比率分别可达16.2068%和10.0431。