Backwardation in Energy Future Markets: Metallgesellschaft Revisited
Bibliographic record
Abstract
In this paper, we revisit the debate on the merits of the stack-and-roll hedging strategy employed by Metallgesellschaft's American subsidiary, MGRM. Since the profitability of this hedging strategy depends on whether or not backwardation was the norm in energy futures contracts, we first provide the evidence on backwardation with an updated data set. We then examine the two major risks that such a hedging strategy faces margin call risk due to price declines and contango risk. Based on the data up to 1992, we find that the strategy could be expected to be profitable while the risks were not very high. Based on the updated data (up to 2000), the program's expected profits are smaller but still significant, however, the risks are higher. The probabilities of encountering a similar problem to the one MGRM faced are twice as high with the updated data than with the data up to 1992. In other words, the risk-return pattern of such a strategy is less appealing now than when MGRM implemented its hedging program.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 source (direct Gemma or distilled Codex), 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".