The Convenience Yield and the Informational Content of the Oil Futures Price
Bibliographic record
Abstract
Recent studies have shown that futures prices do not generally outperform naive no-change forecasts of spot prices, calling into question the usefulness of futures prices for forecasting purposes. However, such usefulness is predicated on the question of whether certain modeling strategies are able to yield more of the information found in futures prices. Applying a forecast-based approach, we study the extent to which alternative ways of modeling futures prices can reveal the extent of the information present in futures prices. Using weekly and monthly data, and futures of maturities of one to four months, we notably examine the out-of-sample predictability of futures prices over various forecast horizons, and in real-time, whereby parameters are updated prior to each sequential forecast. Our results with weekly data are particularly interesting. We find that models allowing for a time-varying convenience yield often produce considerably more precise forecasts over the three forecast horizons considered. Thus, more of the informational content of futures prices is attainable when both the price level and the distance of the latter from spot price are jointly considered, rather than when only the price level is considered. We also document that forecast performances improve with longer date-to-maturity futures, suggesting that the role of the convenience yield is greater when physical oil inventories are held for longer durations. Finally, we show that forecast accuracy is highest at the one year horizon, though the time-varying convenience models have a much higher accuracy than unit-root-based models even over the three and five-year horizons.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".