Risky times: Seasonality and event risk of commodities
针对小麦、玉米和大豆，提出一种基于广义岭回归的季节性过滤器，结合成分GARCH模型将季节性风险、事件风险与短期风险动态整合，显著优于标准GARCH(1,1)的样本外风险预测，对风险管理和投资组合构建有参考价值。
Abstract The seasonal risk of wheat, corn, and soybean is modeled by a novel seasonality filter based on a generalized ridge regression. Then, using a component GARCH model, seasonal risk is combined with event risk and a short‐term risk dynamics. The resulting model is robust, generates seasonal patterns related to the crop cycle, and significantly outperforms the standard GARCH(1,1) in terms of out‐of‐sample risk prediction. Results are relevant for risk management and portfolio construction.