Was there a peso problem in cattle options?
Bibliographic record
Abstract
Purpose – Pricing densities implied from options on live cattle futures show a persistent and negative skew. The purpose is to examine whether the skew can be explained, in part, by peso-type problems. Design/methodology/approach – Two announcements of bovine spongiform encephalopathy (BSE) provide a natural setting within which to examine the validity of the peso-problem explanation. These announcements represent the first documented cases of BSE in North America. Prior to the announcements, the potential for BSE would have been known by market participants as the disease had been found among cattle in the British Isles, Europe and Asia. The paper uses options market data to compute implied moments of the pricing distribution for live cattle futures. The paper then analyzes these moments around BSE announcements. Findings – The first Canadian BSE announcement impacted the mean and volatility but not the implied skew. Later in the year, BSE was found in a US cow and the paper finds a statistically significant change in the implied skew. The distribution showed a pronounced leftward skew prior to the US announcement but was nearly symmetric during the days afterwards. This finding is consistent with the market having priced the possibility of a BSE discovery into deep out-of-the-money put options. Originality/value – Peso problems have been documented in other financial markets. The results are important because they suggest that they may also be important to agricultural markets and that agricultural options markets do account for low probability but highly important events.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".