Skewness Premium in Managed Futures: A Practitioner’s Guide
An Executive Summary for Investors and Investment Professionals
Introduction
Skewness-based managed futures strategies offer a unique opportunity to enhance portfolio performance by exploiting the asymmetry of return distributions across diverse asset classes. By focusing on the third moment of return distributions—skewness—these strategies seek to capitalize on the tendency of assets with negative skewness to offer higher expected returns as compensation for tail risk, and assets with positive skewness to exhibit lottery-like payoffs with lower expected returns. The whitepaper “Skewness Premium in Managed Futures” by ReSolve Asset Management provides a comprehensive practitioner’s perspective, examining the application of skewness strategies across equities, bonds, commodities, and currencies, and highlighting their potential as a diversification tool.
This executive summary distills the key findings of the whitepaper, offering investors and professionals actionable insights into the construction, performance, and diversification benefits of skewness strategies. Historical simulations spanning January 1990 to September 2023 demonstrate that thoughtfully implemented skewness strategies can deliver robust risk-adjusted returns while offering resilience during market stress.
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Understanding Skewness in Managed Futures
Skewness measures the asymmetry of return distributions, revealing the likelihood of extreme outcomes. Return distributions with negative skewness are prone to larger losses, often requiring higher expected returns to compensate investors for this risk, while those with positive skewness may offer lottery-like payoffs, attracting investors despite lower expected returns. The whitepaper emphasizes the economic rationale behind the skewness premium, rooted in investor aversion to tail-risk, and risk-seeking behaviour after lottery gains.
The study explores skewness across 26 asset classes, including currencies, energies, metals, bonds, and equities. Daily data for futures contracts on most of these assets is available from January 1990 to September 2023, providing a comprehensive dataset for analysis. Remaining assets are included once they become available.
Constructing Skewness Strategies
The whitepaper explores a rigorous methodology for constructing skewness-based managed futures strategies, employing both cross-sectional and time-series approaches. Importantly, the skewness strategies trades both long and short based on rolling skewness signals measured over multiple aggregation frequencies and rolling windows. This multi-scale approach enhances estimation stability, capturing both short- and long-term asymmetry dynamics.
Skewness Measurement
Technically, skewness signals are calculated as the third standardized moment of log-return distributions over multiple aggregation frequencies (e.g., 2-day to 10-day intervals) and rolling windows (e.g., 60 to 250 observations over each aggregation frequency). For example, one measure measures skewness over the past 60 2-day return intervals, while another measures skewness over the past 250 10-day return intervals. This multi-scale approach enhances estimation stability, capturing both short- and long-term asymmetry dynamics over multiple resampling periods. The formula used is:
where 
is the number of observations, 
is the log return at time 
, r̄ is the mean, and σ is the standard deviation.
Portfolio Construction
Portfolios are constructed by taking long positions in assets with negative skewness and short positions in those with positive skewness, based on the premise that negative skewness signals higher expected returns. Two weighting schemes are employed:
- Inverse Volatility Weighting: Assets are weighted proportionally to the negative of their skewness and inversely to their 40-day realized volatility.
- Mean-Variance Optimization: Incorporates correlations via a 252-day covariance matrix, with weights optimized to maximize expected returns (negative skewness) relative to portfolio risk, using the formula:
where 
is the weight vector, 
is the covariance matrix, and 
is the signal vector (negative skewness). Covariance estimates are stabilized using Ledoit-Wolf shrinkage.
An ensemble approach averages weights from both methods, with portfolios scaled to a 10% annualized volatility target and rebalanced daily with 5-day smoothing to reduce turnover.
Performance Analysis of Skewness Strategies
The whitepaper provides detailed performance metrics for skewness strategies across sectors and the full futures universe, net of estimated trading costs.
Cross-sectional and time-series strategies are evaluated within sectors (currencies, energies, metals, bonds, equities), with portfolios scaled to 10% annualized volatility. We focus on time-series strategies consistent with how many skewness strategies are implemented in practice.
Figure 4. Performance by Sector for Time-Series Skewness Strategies, January 1, 1990 – September 30, 2024
Data from CSI Data. Authors’ calculations. Performance is HYPOTHETICAL and NET of estimated trading slippage and commissions. Past performance is not indicative of future results. See disclaimers at the end of the document for more information.
Bonds and energy markets exhibit the best performance among sectors, though all sectors produced positive returns over the sample period. Also notice how, with the exception of equity indices, sector skewness strategies have relatively low correlation with respective long-only exposures to the underlying sectors.
Figure 6. Cumulative Excess Returns of Time-Series (TS) Skewness Strategies by Sector, January 1, 1990 – September 30, 2024
Data from CSI Data. Authors’ calculations. Performance is HYPOTHETICAL and assumes $100 invested with reinvestment of profit NET of estimated trading slippage and commissions. Past performance is not indicative of future results. See also disclaimers at the end of the document for more information.
Cumulative returns highlight bonds’ consistent outperformance, while equities and metals lag. It’s clear visually that sector skewness strategies have relatively low correlation to one another, suggesting they may be highly complementary in a portfolio.
Expanding to the full futures universe, the paper explores inverse-volatility weighted and mean-variance-optimized portfolios with several transformations of the raw skewness signal. In addition to raw skewness signals, the paper also considers binary (positive or negative) and rank-transformed signals. All transforms and weighting schemes produced similar results, with rank-based and optimized portfolios performing slightly better in the sample period.
Figure 7. Performance of Time-Series (TS) Inverse Volatility Weighted Skewness Strategies by Signal and Transform, January 1, 1990 – September 30, 2024
Figure 8. Cumulative Excess Returns of Time-Series (TS) Inverse Volatility Weighted Skewness Strategies by Signal and Transform, January 1, 1990 – September 30, 2024
Figure 9. Performance of Time-Series (TS) Optimized Skewness Strategies by Signal and Transform, January 1, 1990 – September 30, 2024
Data from CSI Data. Authors’ calculations. Performance is HYPOTHETICAL and NET of estimated trading slippage and commissions. Past performance is not indicative of future results. See disclaimers at the end of the document for more information.
Figure 10. Cumulative Excess Returns of Time-Series (TS) Optimized Skewness Strategies by Signal and Transform, January 1, 1990 – September 30, 2024
Figure 11. Performance of Time-Series (TS) Optimized Ensemble Skewness Strategies, January 1, 1990 – September 30, 2024
Data from CSI Data. Authors’ calculations. Performance is HYPOTHETICAL and NET of estimated trading slippage and commissions. Past performance is not indicative of future results. See disclaimers at the end of the document for more information.
Figure 12. Cumulative Excess Returns of Time-Series (TS) Optimized Ensemble Skewness Strategies, January 1, 1990 – September 30, 2024
Data from CSI Data. Authors’ calculations. Performance is HYPOTHETICAL and assumes $100 invested with reinvestment of profit NET of estimated trading slippage and commissions. Past performance is not indicative of future results. See also disclaimers at the end of the document for more information.
Diversification Benefits During Market Stress
Skewness strategies have demonstrated compelling diversification benefits, particularly during adverse market conditions. Performance is evaluated during the worst quarterly returns of benchmarks like the S&P 500, Bloomberg 7-10Y Treasury Index, and Société Générale CTA Trend Index, as well as during major drawdowns over the period January 1, 1990 – sept 30, 2023.
The skewness strategies delivered positive returns across most quintiles of benchmark performance, with resilience in the worst periods as demonstrated in Figure 13.
Figure 13. Annualized Excess Returns of Skewness (Net) and S&P 500 Futures Conditioned on S&P 500 Total Return Quintile, January 1, 1990 – September 30, 2024
Data from CSI Data, Standard & Poor’s. Authors’ calculations. S&P 500 futures are GROSS of roll costs. Skewness Strategies performance is HYPOTHETICAL and NET of estimated trading slippage and commissions. Past performance is not indicative of future results. See also disclaimers at the end of the document for more information.
Figure 14. Annualized Excess Returns of Skewness (Net) and U.S. 10Y Treasury Futures Conditioned on Bloomberg 7-10Y Total Return Quintile, January 1, 1990 – September 30, 2024
Data from CSI Data, Bloomberg. Authors’ calculations. U.S. 10Y Treasury futures are GROSS of roll costs. Skewness Strategies performance is HYPOTHETICAL and NET of estimated trading slippage and commissions. Past performance is not indicative of future results. See also disclaimers at the end of the document for more information.
Figure 15. Annualized Excess Returns of Skewness (Net) and Société Générale CTA Trend Index by Performance Quintile, January 1, 1990 – September 30, 2024
Data from Société Générale and authors’ calculations. Strategy and NEIX performance is HYPOTHETICAL and NET of estimated trading slippage and commissions. Past performance is not indicative of future results. See disclaimers at the end of the document for more information.
During the Global Financial Crisis (2007-2012), the skewness strategies offered attractive stability while the S&P 500 futures dropped over 50%. Similar resilience is observed during the Tech Wreck (2000-2006) and bond market drawdowns (e.g., 2022).
Data from CSI Data and Standard & Poor’s. Authors’ calculations. Skewness Strategies performance is HYPOTHETICAL and NET of estimated trading slippage and commissions. Past performance is not indicative of future results. See also disclaimers at the end of the document for more information.
Data from CSI Data and Standard & Poor’s. Authors’ calculations. Skewness Strategies performance is HYPOTHETICAL and NET of estimated trading slippage and commissions. Past performance is not indicative of future results. See also disclaimers at the end of the document for more information.
Data from CSI Data and Standard & Poor’s. Authors’ calculations. Skewness Strategies performance is HYPOTHETICAL and NET of estimated trading slippage and commissions. Past performance is not indicative of future results. See also disclaimers at the end of the document for more information.
Figure 19. Cumulative Excess Growth of U.S. 10Y Treasury Futures and Skewness Strategy (Net) during the 2022 Bond Drawdown, August 5, 2020 – September 30, 2024
Data from CSI Data and Bloomberg. Authors’ calculations. Skewness Strategies performance is HYPOTHETICAL and GROSS of all trading slippage and commissions. Past performance is not indicative of future results. See also disclaimers at the end of the document for more information.
Data from CSI Data and Bloomberg. Authors’ calculations. Skewness Strategies performance is HYPOTHETICAL and GROSS of all trading slippage and commissions. Past performance is not indicative of future results. See also disclaimers at the end of the document for more information.
Data from CSI Data and Bloomberg. Authors’ calculations. Skewness Strategies performance is HYPOTHETICAL and GROSS of all trading slippage and commissions. Past performance is not indicative of future results. See also disclaimers at the end of the document for more information.
Data from Société Générale and authors’ calculations. Skewness Strategies performance is HYPOTHETICAL and NET of estimated trading slippage and commissions. Past performance is not indicative of future results. See disclaimers at the end of the document for more information.
Data from Société Générale and authors’ calculations. Skewness Strategies performance is HYPOTHETICAL and NET of estimated trading slippage and commissions. Past performance is not indicative of future results. See disclaimers at the end of the document for more information.
Data from Société Générale and authors’ calculations. Skewness Strategies performance is HYPOTHETICAL and NET of estimated trading slippage and commissions. Past performance is not indicative of future results. See disclaimers at the end of the document for more information.
The strategies maintained low correlations with traditional benchmarks (S&P 500, U.S. 10Y Treasuries) and other managed futures strategies (CTA Trend Index), enhancing its diversification appeal.
Figure 25. Rolling 36-Month Pearson Correlations of Skewness Strategy (Net) vs. S&P 500, U.S. 10-Year Treasury Futures, and Société Générale CTA Trend Index, January 1, 1990 – September 30, 2024
Data from CSI Data, Standard & Poor’s, Société Générale. Authors’ calculations. Skewness Strategies performance is HYPOTHETICAL and NET of estimated trading slippage and commissions. Past performance is not indicative of future results.
While gross returns are promising, net performance after transaction costs is critical. The whitepaper accounts for slippage and commissions, with strategies like weight smoothing reducing turnover. Ensemble approaches further mitigate costs by diversifying signal reliance, ensuring practical viability.
Skewness-based managed futures strategies may offer a powerful tool for enhancing risk-adjusted returns and portfolio diversification. The whitepaper demonstrates their ability to deliver attractive standalone returns (e.g. 7.89% annualized for the strategy) and resilience during market stress, with low correlations to traditional assets. These characteristics suggest that skewness strategies can be a valuable complement to diversified portfolios, particularly for investors seeking to manage tail risk.
However, implementation requires careful consideration of transaction costs, estimation robustness, and market dynamics. Investors should approach skewness strategies as part of a broader allocation, consulting with advisors to tailor their application to specific goals and risk tolerances.
Disclaimers
Hypothetical Performance Disclosure The performance figures presented are hypothetical and do not represent actual trading results. They are based on backtested data and assumptions, including the availability and accuracy of historical data. Actual results may vary significantly. Past performance is not indicative of future results.
Risk Disclosures Futures trading involves substantial risk of loss and is not suitable for all investors. Risks include but are not limited to, derivatives risk, liquidity risk, leverage risk, and operational risk. Investors should carefully assess their objectives and risk tolerance before engaging in such strategies.
General Disclaimers This summary is for informational purposes only and should not be considered as investment advice nor does it constitute an offer to sell or a solicitation to buy any security or investment product. It is not tailored to individual investment needs. Investors are encouraged to seek professional advice before making decisions.
Confidential and proprietary information The contents hereof may not be reproduced or disseminated without the express written permission of ReSolve Asset Management Inc. ReSolve Asset Management Inc. is registered as an Investment Fund Manager and Portfolio Manager in multiple jurisdictions and with the SEC and NFA in the U.S., specializing in quantitative strategies like skewness-based managed futures.