The Impact of Crude Oil Price Shocks on Bitcoin under the Russian–Ukrainian War
Bibliographic record
Abstract
Nowadays, the Russian-Ukrainian war has been a hotly topic, and the war has shaken the global economy, especially in the international crude oil market. Also, as a popular financial instrument, the investors like to see Bitcoin as a hedging tool, but the problem of whether cryptocurrencies can hedge the volatility of commodity markets lacks a unified explanation. Therefore, the paper wants to find the relationship between Bitcoin and crude oil during the Russian-Ukrainian war. This paper uses data from Bitcoin, crude oil WTI futures, and crude oil Brent futures, and constructs the VAR model and ARMA-GARCH model based on these data. Ultimately, the article finds that the volatility of the international crude oil market only has little impact on Bitcoin. Thus, the investors do not need to worry about the high crude oil price caused by the war will affect Bitcoin’s yield and volatility, so Bitcoin seems like a great hedging instrument against the shock of the international crude oil market.
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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".