Multi-Task Forecasting of the Realized Volatilities of Agricultural Commodity Prices

Motivated by the comovement of realized volatilities (RVs) of agricultural commodity prices, we study whether multi-task forecasting algorithms improve the accuracy of out-of-sample forecasts of 15 agricultural commodities during the sample pe- riod from July 2015 to April 2023. We consider alternative multi-task stacking algorithms and variants of the multivariate Lasso estimator. We find evidence of in-sample predictability, but hardly evidence that multi-task forecasting improves out-of-sample forecasts relative to a classic univariate heterogeneous autoregres- sive (HAR) RV model. We also study an extended model that features the RVs of energy commodities and precious metals.