Bitcoin and crypto-mining stocks: A quantile connectedness approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper, we studied the extreme connectedness between Bitcoin and crypto-mining stocks using the quantile connectedness approach of Ando et al. (2022). We estimated the connectedness (i.e., the direction and strength of spillover effects) at the median, extreme lower, and extreme upper quantiles. Our results revealed a highly interconnected system, with Bitcoin identified as a net transmitter of shocks. RIOT and MARA also emerged as major net transmitters in the system, while GREE and NILE were net receivers. The spillover effects were more pronounced during extreme market conditions compared to normal conditions. Moreover, the connectedness of the system progressively increased, peaking in 2021 when China banned crypto-mining. The extreme and dynamic connectedness identified in this study offers valuable insights for investors regarding hedging strategies and portfolio allocation, as well as for regulators focused on financial stability and systemic risk.
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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 | 0.000 |
| 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 it