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Risk-neutral skewness and commodity futures pricing

Fuertes, A-M. ORCID: 0000-0001-6468-9845, Liu, Z. & Tang, W. (2022). Risk-neutral skewness and commodity futures pricing. Journal of Futures Markets, 42(4), pp. 751-785. doi: 10.1002/fut.22308


This paper investigates the predictive content of risk-neutral skewness (RNSK) for the dynamics of commodity futures prices. A trading strategy that buys futures with positive RNSK and sells futures with negative RNSK generates a significant excess return. Unlike traditional commodity risk factors’ signals, the positive return generated from the RNSK signal is more pronounced in the contango phase. After controlling traditional commodity risk factors, the RNSK signal exhibits a more stable and prolonged predictive ability. The directional-learning hypothesis explains the RNSK impact when commodity futures show higher idiosyncratic risks and illiquidity (positive RNSK) and overpriced (negative RNSK).

Publication Type: Article
Additional Information: This is the peer reviewed version of the following article: Fuertes, A.-M., Liu, Z., & Tang, W. (2022). Risk-neutral skewness and commodity futures pricing. Journal of Futures Markets, 1– 35, which has been published in final form at This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited.
Publisher Keywords: Commodity futures; Asset pricing; Skewness; Risk-Neutral; Risk Factors
Subjects: H Social Sciences > HD Industries. Land use. Labor
H Social Sciences > HG Finance
Departments: Bayes Business School > Finance
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