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

Moritz Rettinger, Christian Mandl, Stefan Minner

Publikation: Beitrag in FachzeitschriftArtikelBegutachtung

1 Zitat (Scopus)

Abstract

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.

OriginalspracheEnglisch
Seiten (von - bis)341-360
Seitenumfang20
FachzeitschriftEuropean Journal of Operational Research
Jahrgang316
Ausgabenummer1
DOIs
PublikationsstatusVeröffentlicht - 1 Juli 2024

Fingerprint

Untersuchen Sie die Forschungsthemen von „A data-driven approach for optimal operational and financial commodity hedging“. Zusammen bilden sie einen einzigartigen Fingerprint.

Dieses zitieren