Commodity Option Return Predictability

Constant Aka, Marie‐Hélène Gagnon and Gabriel J. Power

Journal of Futures Markets, 2025, vol. 45, issue 10, 1544-1578

Abstract: This paper investigates the predictability of delta‐hedged commodity option returns using 103 predictors. We estimate several linear and nonlinear machine learning models and forecast ensembles using futures options data on seven commodities. There is strong evidence of out‐of‐sample return predictability for horizons of 1 week to 1 month ahead. We show how a machine learning‐informed long‐short option trading strategy generates positive returns after transaction costs for most commodities. Among the groups of predictors, options‐based characteristics are the most informative, but macroeconomic variables typically improve forecasts. A nonlinear ensemble forecast provides the best results, while the best single model is the Random Forest. Some machine learning models perform poorly. Finally, we document strong evidence for increased predictability in periods of high volatility.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1002/fut.22614

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wly:jfutmk:v:45:y:2025:i:10:p:1544-1578

Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=0270-7314

Access Statistics for this article

Journal of Futures Markets is currently edited by Robert I. Webb

More articles in Journal of Futures Markets from John Wiley & Sons, Ltd.
Bibliographic data for series maintained by Wiley Content Delivery ().