Causality between Arbitrage and Liquidity in Platinum Futures
Bibliographic record
Abstract
Arbitrage and liquidity are interrelated. Liquidity facilitates arbitrageurs’ trading on deviations from the law of one price. However, whether arbitrage opportunity leads to an increase or decrease in liquidity depends on the cause of the deviation. A demand shock leads to greater liquidity, while asymmetric information is toxic to liquidity. We examine how arbitrage and liquidity influence each other in the world’s largest platinum futures markets on exchanges in New York and Tokyo. The markets provide an interesting institutional setting because the futures are based on an identical underlying commodity but exhibit different liquidity characteristics both intraday and over their lifespans. Using intraday data, we find that deviation in currency-adjusted futures prices leads, on average, to an immediate increase in liquidity, suggesting that demand shocks are the dominant driver of arbitrage opportunities. Less actively traded futures experience a greater liquidity effect. Arbitrageurs improve liquidity in both New York and Tokyo by acting as discretionary liquidity traders and cross-sectional market-makers.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".