Commodity Price Forecasts, Futures Prices, and Pricing Models
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Even though commodity-pricing models have been successful in fitting the term structure of futures prices and its dynamics, they do not generate accurate true distributions of spot prices. This paper develops a new approach to calibrate these models using not only observations of oil futures prices, but also analysts’ forecasts of oil spot prices. We conclude that to obtain reasonable expected spot curves, analysts’ forecasts should be used, either alone or jointly with futures data. The use of both futures and forecasts, instead of using only forecasts, generates expected spot curves that do not differ considerably in the short/medium term, but long term estimations are significantly different. The inclusion of analysts’ forecasts in addition to futures, instead of only futures prices, does not alter significantly the short/medium part of the futures curve but does have a significant effect on long-term futures estimations. This paper was accepted by Gustavo Manso, finance.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it