Bibliographic record
Abstract
ABSTRACT In this study, we explore price discovery across the following three Bitcoin markets: spot, futures, and exchange‐traded funds (ETFs). Employing the fractionally cointegrated vector autoregressive (FCVAR) model, we estimate price discovery in each market using minute‐level price data from October 19, 2021, the launch date of the first US Bitcoin futures‐based Bitcoin ETF, to December 30, 2022. The trivariate FCVAR analysis reveals that the three markets are pairwise cointegrated. In the spot‐futures pair, the spot market emerges as the dominant force in price discovery, while in the spot–ETF pair, the ETF market assumes a leading role. Our paper is the first to show the importance of the newly introduced Bitcoin ETF market in the price discovery process. Extending the analysis to the more recent period, we find that the approval of spot‐based Bitcoin ETFs has weakened the price discovery contribution of the futures‐based ETF and Bitcoin spot market has since become the dominant venue for price discovery.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".