The effect of multiple listings on the bid–ask spread in option markets: The case of Montreal Exchange
Bibliographic record
Abstract
Abstract In this article, we examine the effect of multiple listings of options on their bid–ask spread, by comparing options contracts listed only on the Montreal Exchange with those interlisted on that exchange and on a U.S. exchange as well. Using a statistical procedure adapted to panel data and two models for the determination of the bid–ask spread, we find that the bid–ask spreads of Montreal options interlisted in U.S. markets are narrower than those of non‐interlisted options. That advantage tends to disappear, however, with an increase in option price and to increase with its volatility, but is not affected by the volume of transactions in the option market. The analysis also shows that interlisting may result in time lags in the convergence of quotes between Montreal and the U.S. markets. Moreover, our evidence shows that with interlisting, volume shifts to the option market where trading in the underlying security is concentrated, irrespective of the location where the option was first introduced. © 2002 Wiley Periodicals, Inc. Jrl Fut Mark 22:939–957, 2002
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 | 0.007 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".