Oil Prices: Heavy Tails, Mean Reversion and the Convenience Yield
Why this work is in the frame
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Bibliographic record
Abstract
Empirical research on oil price dynamics for modeling and forecasting purposes has brought forth several unsettled issues. Indeed, statistical support is claimed for various models of price paths, yet many of the competing models differ importantly with respect to their fundamental temporal properties. In this paper, we study one such property that is still debated in the literature, namely mean-reversion, with focus on forecast performance. Because of their impact on mean-reversion, we account for non-constancies in the level and in volatility. Three specifications are considered: (i) random-walk models with GARCH and normal or student-t innovations, (ii) Poisson-based jump-diffusion models with GARCH and normal or student-t innovations, and (iii) mean-reverting models that allow for uncertainty in equilibrium price and for time-varying convenience yields. We compare forecasts in real time, for 1, 3 and 5 year horizons. For the jump-based models, we rely on numerical methods to approximate forecast errors. Results based on future price data ranging from 1986 to 2007 strongly suggest that imposing the random walk for oil prices has pronounced costs for out-of-sample forecasting. Evidence in favor of price reversion to a continuously evolving mean underscores the importance of adequately modeling the connvenience yield.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it