Strategy #0000000045 · Commodity factors and higher moments
Commodity futures skewness premium
Rank commodity-futures return skewness: long the more negatively skewed basket and short the more positively skewed basket.
A skewness-premium strategy ranks commodity futures by the third standardised moment of past returns. Representative research reports a return spread between negatively skewed longs and positively skewed shorts, but this is not a universal law or proof that skewness causes future returns. Sector concentration, futures rolls, margin and tail moves can dominate.
Historical skewness is backward-looking; window choice, outliers, rolls and sector clustering can change ranks.
Three ways in
Understand the story. Then inspect the risk.
Start with a familiar situation, check the payoff, then test how the numbers change.
The idea · in two minutes
Can cross-sectional differences in past return skewness form a long-short scenario after costs?
Jack ranks commodity returns by skewness over a lookback window. Rose first fixes the window, frequency and outlier policy so no post-formation data leak in.
Jack buys a basket with the lowest skewness and shorts one with the highest. The simulator accepts assumed holding-period returns and basket weights, then deducts total costs.
If the legs concentrate in different sectors, the result may be an energy or agriculture bet. Point-in-time data, continuous-contract adjustments, roll rules and out-of-sample tests are essential.
- Negative-skew long basket assumptionCompare assumed returns for the lowest-skew long basket and highest-skew short basket, less costs.
- Positive-skew short basket assumptionInputs are not market data; no realised skew ranks, historical portfolios, futures rolls, weight constraints or margin backtest.
- Long-short less assumed costsIllustrative expiry outcome.
Illustrative research examples only. They are not live signals or a recommendation.
The mechanics · payoff and exposure
S_i=\frac{1}{T\sigma_i^3}\sum_{t=1}^{T}(R_{i,t}-\bar R_i)^3,\quad R_{LS}=w_LR_L-w_SR_S-C
Sᵢ is standardised sample skewness over the formation window. RLS is the assumed return of the lowest-skew long basket minus the highest-skew short basket. Define weight normalisation and rolls in advance.
Greeks: the directional intuition
- 偏態估計
- Measures sensitivity to implied volatility.
- 商品曝險
- Measures sensitivity to implied volatility.
- 賣空與保證金
- Measures sensitivity to implied volatility.
S_i=\frac{1}{T\sigma_i^3}\sum_{t=1}^{T}(R_{i,t}-\bar R_i)^3,\quad R_{LS}=w_LR_L-w_SR_S-C
Practical context · published observations
Commodity futures skewness premium: read the record with its caveats.
The chart uses the published figures or explicitly labelled study inputs below. A historical index or model replay is not the same as an investor’s realised account return.
輸入不是市場資料；沒有實際偏態估計、分組歷史、期貨展期、權重限制或保證金回測。 Source ↗ · Methodology ↗
Change assumptions and inspect the scenario
Inputs illustrate the strategy mechanics; they are not live quotes, forecasts or investment advice.
Variants · same family, different risk levers
Related methods change the mechanism or risk.
Compare their signal, hedge and model assumptions.
| Related strategy | Relationship | What changes | New cost or risk |
|---|---|---|---|
| Coskewness | Market co-skewness | Measures joint tail movement with the market. | Systematic factor exposure differs from standalone skewness. |
| Maximum daily return rank | Lottery-like payoff | Uses the largest daily return in a month as a right-tail proxy. | A single observation is noisy. |
| Implied skewness premium | Options market | Compares risk-neutral with physical skewness. | Option-chain and replication errors. |
Inputs are not market data; no realised skew ranks, historical portfolios, futures rolls, weight constraints or margin backtest.
Read beyond the summary
References
Primary sources and research used to describe the strategy and its limits.