Recent theoretical literature argues that total skewness matters to the pricing of assets, assets with higher degrees of skewness or lottery-like features should earn lower expected returns. The impact of skewness on asset’s future returns has been investigated mainly for equities and options, but researches are moving to review also other assets.

A recent article by Perez, Frijns, Fuertes, and Miffre studies the pricing of skewness in commodity futures markets. The time-series tests show that a fully-collateralized portfolio that buys commodities with low skewness and shorts commodities with high skewness earns 8.01% a year with a t-statistic of 4.08. The performance of the low-minus-high skewness portfolio is not fully explained by a standard commodity benchmark, which makes it a perfect addition to commodity-based multi-strategy portfolios.

Fundamental reason

Academic studies explain skewness effect in all assets by investors are willing to pay a higher price, and to earn a lower expected return, on assets with lottery-like payoffs. A recent study shows that skewness affects commodity futures returns and documents that skewness is priced in different markets than the ones where retail investors typically trade. Hence, its findings suggest that the effect of skewness on asset returns is not merely a consequence of retail investor preferences. Rather this indicates that other groups of traders have these heterogeneous preferences.

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Markets Traded
commodities

Financial instruments
CFDs, futures

Confidence in anomaly's validity
Strong

Backtest period from source paper
1987-2014

Notes to Confidence in Anomaly's Validity

Indicative Performance
8.01%

Period of Rebalancing
Monthly

Notes to Indicative Performance

per annum, data from table 3 for long-short portfolio


Notes to Period of Rebalancing

Estimated Volatility
10.2%

Number of Traded Instruments
10

Notes to Estimated Volatility

data from table 3 for long-short portfolio


Notes to Number of Traded Instruments

Maximum Drawdown
-29.73%

Complexity Evaluation
Simple strategy

Notes to Maximum drawdown

data from table 3 for long-short portfolio


Notes to Complexity Evaluation

Sharpe Ratio
0.79

Simple trading strategy

The investment universe consists of 27 futures contracts on commodities. Each month, investor calculates skewness (3rd moment of returns) from daily returns from data going 12 months into the past for all futures. Commodities are then sorted into quintiles and investor goes long quintile containing the commodities with the 20% lowest total skewness and short quintile containing the commodities with the 20% highest total skewness (over a ranking period of 12 months). The resultant portfolio is equally weighted and rebalanced each month.

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.

Source paper
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.

Strategy's implementation in QuantConnect's framework (chart+statistics+code)
Other papers
Han, Mo, Su, Zhu: Idiosyncratic Skewness or Coskewness? Evidence from Commodity Futures Returns
- 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.

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