Air pollution, weather factors, and realized volatility forecasts of agricultural commodity futures

Abstract

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.