Price clustering in the CAC 40 index options market
Bibliographic record
Abstract
We examine in details the pattern and systematic tendencies of clustering in CAC 40 index option transaction prices during the period 1997 to 1999. Similar to extant studies in many financial markets, there is evidence of strong clustering at full index points and option prices are 90% more likely to end with the digit 0 (multiples of 10) than with the digit 5. While the 1999 contract downsizing led to some reduction in clustering at full index point, the basic pattern of clustering remains intact. The pattern of clustering rejects the attraction theory, but is consistent with the notion of cost recovery by market makers. We find important drivers for CAC 40 index option price clustering, namely, the level of option premium, option volume and underlying asset volatility. Higher premium level, higher asset volatility and lower volume are seen to increase option price clustering. We also observe a U-shaped pattern of clustering on an intra-day and intra-year basis. The option premium and volatility effects are consistent with a price level effect. The volatility effect also lends support to the notion of cost recovery by market makers. The volume effect likely represents a liquidity effect and is consistent with the Price Precision Hypothesis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".