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
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How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".