Abstract:
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 period 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 scarce evidence that multi-task forecasting improves
out-of-sample forecasts relative to a classic univariate heterogeneous autoregressive (HAR)-RV model.
This lack of systematic evidence of out-of-sample forecasting gains is corroborated by extensive
robustness checks, including an in-depth study of the quantiles of the distributions of the RVs
and subsample periods that account for increases in the total spillovers among the RVs. We also
study an extended model that features the RVs of energy commodities and precious metals, but our
conclusions remain unaffected. Besides offering important lessons for future research, our results
are interesting for financial market participants, who rely on accurate forecasts of RVs when solving
portfolio optimization and derivatives pricing problems, and policymakers, who need accurate
forecasts of RVs when designing policies to mitigate the potential adverse effects of a rise in the RVs
of agricultural commodity prices and the concomitant economic and political uncertainty.
