Cryptocurrency under Local Conflict: Evidence from Soaring Crude Oil Price
Bibliographic record
Abstract
The ongoing Russian invasion of Ukraine is signaled as a black swan event with major effects to the world. The consequence of the corresponding sanctions sparked short-term and long-term damages not only to the countries directly involved but also to the global economy. Namely, the soaring crude oil price pushes countries to economic crisis. Additionally, the emergence of the cryptocurrency becomes a centerpiece in this conflict, as Ukraine opens cryptocurrency donations and Russia tries to avoid sanction with cryptocurrency. The relationship between the traditional Crude oil market and the relatively new global market of cryptocurrency has sparked this paper to research the effect of this local conflict. In this paper, Crude Oil yields, Bitcoin yields, and Ethereum yields from June 2021 to Sep 2022 are extracted. VAR model and ARMA-GARCHX model are selected to analyze the data. This study intends to examine the relationship between the oil yields and the major cryptocurrency yields, namely Bitcoin and Ethereum, with hopes to forecast the corresponding cryptocurrency yield followed by an oil yield shock.
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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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".