A data-driven approach for optimal operational and financial commodity hedging

Commodity price risk management has been subject to various modeling and optimization approaches. Recently, data-driven policies focusing on the decision rather than prediction quality have been developed to overcome price model misspecification. Yet, in the context of data-driven commodity purchasi...

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Bibliographic Details
Published in:European journal of operational research Vol. 316; no. 1; pp. 341 - 360
Main Authors: Rettinger, Moritz, Mandl, Christian, Minner, Stefan
Format: Journal Article
Language:English
Published: Elsevier B.V 01.07.2024
Subjects:
Commodity procurement
Data-driven optimization
Decision analysis
Model generalization
Commodity procurement
Decision analysis
Model generalization
Data-driven optimization
ISSN:0377-2217
Online Access:Get full text
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Summary:Commodity price risk management has been subject to various modeling and optimization approaches. Recently, data-driven policies focusing on the decision rather than prediction quality have been developed to overcome price model misspecification. 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 independent of the derivative’s maturity. A mixed-integer programming model is developed to train policy parameters efficiently. 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 ten years and show that the average savings potential of a combined financial and operational procurement policy compared to single-instrument 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 by leveraging available financial instruments. Best model results are identified for a decision granularity with fewer parameters as high-frequent decisions deteriorate learning stability and model generalization. •Model generalization for purchasers commanding operational and financial instruments.•Quantification of the value of both decision modes for commodity-purchasing firms.•Demonstration of the general setting’s implications for the optimal policy structure.•Independent demand and decision period granularities (e.g., daily, weekly, and monthly).
ISSN:0377-2217
DOI:10.1016/j.ejor.2024.01.026