A Data-Driven Approach for Optimal Operational and Financial Commodity Hedging

Commodity price risk management has been subject to various stochastic modeling approaches. Recently, data-driven policies focusing on decision rather than prediction quality are developed to overcome price model misspecifications. Yet, in the context of data-driven commodity purchasing, the existing literature either considers anticipatory inventory management or forward contracting where the decision frequency corresponds to the maturity of the traded contracts. We prove the optimality of a novel procurement policy combining operational and financial instruments with decision granularities agnostic to the derivative's maturity. A mixed-integer programming model is developed to efficiently train policy parameters. We study the implications of policy complexity for learning-stability and out-of-sample generalization.Finally, we backtest the data-driven policy on real market data of four major commodities (i.e., copper, nickel, corn, and soybean) over a period of ten years and show that the average savings potential of a combined financial and operational procurement policy compared to single mode strategies is up to 6.38% for corn where warehousing can efficiently mitigate price seasonality. The approach hedges corn and soybean commodities more efficiently through inventories while copper and nickel can be hedged efficiently leveraging available financial instruments. Best model results are identified for a lower decision granularity and parametrization as high-frequent decisions deteriorate learning stability and model generalization