Aggregate illiquidity and crypto option returns
Bibliographic record
Abstract
We examine how market makers’ inventory imbalances contribute to aggregate illiquidity in the Bitcoin options market. Using high-frequency trade and quote data from Deribit between January 2021 and December 2024, we develop a measure of aggregate gamma inventory (AGI) that reflects the extent of dynamic rebalancing market makers require to manage their exposures. When AGI is negative, market makers trade in the same direction as BTC price momentum, amplifying price pressure and increasing trading costs. Option spreads widen as AGI becomes more negative, consistent with rising costs of maintaining near delta-neutral positions. On average, a one-standard-deviation decrease in AGI is associated with a 0.12% (0.19%) increase in effective spreads for out-of-the-money calls (puts). We estimate a dynamic factor model and show that illiquidity loads significantly on the first latent factor priced in BTC option returns. These findings underscore the role of illiquidity in nascent derivatives markets.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".