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This paper examines a skewness-based trading strategy applied to 22 commodity futures. Assets are ranked monthly by historical return skewness, and a long–short portfolio is formed by buying low-skewness commodities and shorting high-skewness ones, with equal weighting and monthly rebalancing. The strategy is tested in long, short, and combined variants, and shows robust performance across different specifications and time windows. Robustness checks confirm that skewness is a stable cross-sectional predictor of returns, while adding momentum does not improve results.
Fundamental reason
The skewness effect in commodities exists because markets systematically misprice asymmetric risk. Investors overpay for positively skewed, “lottery-like” assets, leading to lower future returns, while negatively skewed assets are underpriced and offer a risk premium for bearing crash risk. In addition, commodity markets are influenced by hedging pressure and structural imbalances, where producers and consumers hedge uneven risks, reinforcing pricing distortions. Since arbitrage is limited by tail risk and constraints on shorting, these inefficiencies persist and remain exploitable.
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Market Factors
Confidence in Anomaly's Validity
Period of Rebalancing
Number of Traded Instruments
Notes to Number of Traded Instruments
Complexity Evaluation
Financial instruments
Backtest period from source paper
Indicative Performance
Notes to Indicative Performance
Estimated Volatility
Notes to Estimated Volatility
Maximum Drawdown
Notes to Maximum drawdown
Sharpe Ratio
Regions
Simple trading strategy
Each month, the investor calculates 12-month historical skewness for 22 commodity futures. The commodities are then ranked cross-sectionally. The best portfolio is constructed by going long a small number (3) of commodities with the lowest skewness (i.e., most negatively skewed returns) and simultaneously going short the same number (3) of commodities with the highest skewness (i.e., most positively skewed returns). Each position is equally weighted, and the portfolio is rebalanced monthly. The economic intuition is that assets with high positive skewness tend to be overpriced due to investor demand for “lottery-like” payoffs, while negatively skewed assets compensate investors with higher expected returns due to tail-risk exposure. This creates a market-neutral spread that captures the return difference between underpriced and overpriced skewness profiles in commodities.
Hedge for stocks during bear markets
Yes – Based on the source research paper (see Appendix B), the strategy has a significantly negative correlation to the equity market; therefore, it probably can be used as a hedge/diversification to equity market risk factor during bear markets.
Out-of-sample strategy implementation in QuantConnect (chart, statistics & code)
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Source paper
Dujava, Cyril and Vojtko, Radovan: An Evaluation of the Skewness Model on 22 Commodities Futures
Abstract: Skewness is one of the less-known but practical measures from statistics that can be used in trading. It is defined as a measure of the asymmetry of the probability distribution of a random variable around its mean. Financial mathematics and most quantitative models assume some kind of symmetric distribution of random variables, such as near-normal distribution, which would have zero skewness. From the perspective of traders, it is always interesting to look for extreme values of skewness which attributed to non-expected outcomes. When you bet a reasonable amount of capital on non-quite-probable but good-calculated events, it is possible to catch a chunk of those tails risks while also possible to hedge harmful exposure (if you have some of the opposite exposure). But is using skewness as a factor in a systematic strategy really profitable? The goal of this analysis is to explore the commodity skewness trading strategy and perform the battery of robustness tests to see how sensitivity analysis changes overall results regarding performance, volatility, and Sharpe ratios. We have covered the topic of robustness testing topic extensively; we also mentioned the lottery effect and possible asymmetric payoffs that make skewness as a predictor an especially lucrative topic for trading strategies. We encourage you to look at similar of our past posts on our Blog.
Other papers
Fernandez-Perez, Frijns, Fuertes, Miffre: Commodities as Lotteries: Skewness and the Returns of Commodity Futures
Abstract: This article studies the relation between skewness and subsequent returns in commodity futures markets. Systematically buying commodities with low skewness and shorting commodities with high skewness generates a significant excess return of 8% a year, which is not merely a compensation for the risks associated with backwardation and contango. Skewness is also found to explain the cross-section of commodity futures returns beyond exposures to the backwardation and contango risk factors previously identified. These results are robust to various alternative specifications and extend the documented importance of skewness in the equity market to the commodity futures markets.
Han, Yufeng and Mo, Xuan and Su, Zhi and Zhu, Yifeng: Is Idiosyncratic Asymmetry Priced in Commodity Futures?
Abstract: We examine the ability of idiosyncratic skewness and coskewness to explain the cross section of commodity returns at the characteristics and factor levels, and find that idiosyncratic skewness is significantly related to the cross section of commodity returns, whereas coskewness is not. Furthermore, we construct a tradeable factor based on idiosyncratic skewness and find that it is significantly priced cross-sectionally in commodity futures. In addition, a new measure of idiosyncratic skewness (IE) proposed by Jiang, Wu, Zhou, and Zhu (2018) is stronger and more robust in capturing the skewness or asymmetry effect at both the characteristics and factor levels.
Yang, Huan and Cai, Jun and Frijns, Bart and Webb, Robert I.: Expected Skewness, Forecast Combination, and Commodity Futures Returns
Abstract: In this paper, we construct ex-ante measures of skewness from ten major commodity futures contract characteristics, including lagged skewness. We first employ monthly cross-sectional regressions of skewness on several lagged contract characteristics. Second, we follow a forecast combination approach and run monthly cross-sectional regressions of skewness on individual contract characteristics. Both approaches generate expected skewness that is significantly and negatively correlated with commodity futures contract returns, even when we construct expected skewness without using lagged skewness. Our empirical evidence, therefore, provides strong support for the key prediction of Barberis and Huang's (2008) model relating asset return skewness to asset returns.
Padysak, Matus and Vojtko, Radovan: Trading Strategy for Bear Markets
Abstract: This paper aims to find a strategy that would work even during bear markets. Such approach should be profitable even when the equity markets are down and could be used as a hedge during those bad times. Common sense suggests that maybe some different asset classes could be used for such purpose. Therefore this paper examines the relationship between prices and skewness of commodities from the practitioner's point of view, where such idea is based on something similar in the world of equities, the Lottery effect in the stocks. Individual investors tend to prefer stocks with lottery-like payoffs in the search for the as high profits as it is possible, and they are willing to play the equity lottery. Unfortunately, in the lotteries, there is a small number of winners, a large number of losers, and one happy lottery ticket issuer that has profited from it. Studies have found out that stocks with lottery-like payoffs have negative abnormal returns if they are compared to the stocks with non-lottery-like payoffs. The same results are found in the world of commodities, where the lottery-like characteristics can be measured by skewness. Most importantly, such a strategy consisting of going long four commodities with the lowest skewness and shorting four commodities with the highest skewness is profitable and negatively correlated with the equity market. It also survives various trading assumptions and trading costs, while remaining profitable.