JOURNAL ARTICLE
Does Speculation in Futures Markets Improve Commodity Hedging Decisions?
Published In: Management Science (INFORMS), 2026, v. 72, n. 3. P. 2525 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Fernandez-Perez, Adrian; Fuertes, Ana-Maria; Miffre, Joëlle 3 of 3
Abstract
This article analyzes the comparative effectiveness of traditional minimum-variance hedging versus selective hedging strategies in commodity futures markets. Traditional hedging focuses solely on minimizing spot price risk by assuming zero expected futures returns, while selective hedging incorporates speculative elements by forecasting futures returns using various methods, including historical averages, autoregressive models, vector autoregressive models, forecast combinations, style-integration approaches, and machine learning techniques. Empirical results across 24 commodities show that selective hedging generally fails to outperform traditional hedging in expected utility gains, primarily due to the limited out-of-sample predictability of commodity futures returns and increased portfolio risk from speculative components. These findings hold robustly across different hedge ratio specifications, estimation windows, rebalancing frequencies, futures maturities, risk aversion assumptions, and sample periods, leading to the recommendation that commodity firms prioritize traditional hedging strategies without speculative forecasting.
Additional Information
- Source:Management Science (INFORMS). 2026/03, Vol. 72, Issue 3, p2525
- Document Type:Article
- Subject Area:Business and Management
- Publication Date:2026
- ISSN:0025-1909
- DOI:10.1287/mnsc.2024.04940
- Accession Number:192085234
- Copyright Statement:Copyright of Management Science (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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