TY - JOUR
T1 - A data-driven approach for optimal operational and financial commodity hedging
AU - Rettinger, Moritz
AU - Mandl, Christian
AU - Minner, Stefan
N1 - Publisher Copyright:
© 2024 The Author(s)
PY - 2024/7/1
Y1 - 2024/7/1
N2 - 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.
AB - 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.
KW - Commodity procurement
KW - Data-driven optimization
KW - Decision analysis
KW - Model generalization
UR - http://www.scopus.com/inward/record.url?scp=85185186816&partnerID=8YFLogxK
U2 - 10.1016/j.ejor.2024.01.026
DO - 10.1016/j.ejor.2024.01.026
M3 - Article
AN - SCOPUS:85185186816
SN - 0377-2217
VL - 316
SP - 341
EP - 360
JO - European Journal of Operational Research
JF - European Journal of Operational Research
IS - 1
ER -