Affiliation:
1. School of Business Administration South China University of Technology Guangzhou Guangdong China
2. School of Finance Guangdong University of Foreign Studies Guangzhou Guangdong China
3. Southern China Institute of Fortune Management Research Guangzhou Guangdong China
4. Institute of Financial Openness and Asset Management Guangzhou Guangdong China
Abstract
AbstractThis 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.
Subject
Economics and Econometrics,Finance,General Business, Management and Accounting,Accounting