The Russia-Ukraine Conflict, Crude Oil Price, and Transportation Industry Yield
Why this work is in the frame
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Bibliographic record
Abstract
The Russia-Ukraine Conflict had a serious impact on the economy of Russia and Ukraine and even the world, among which oil, banking, entertainment, and other industries were hit hard. Through the fluctuation of the transportation industry index during the Russia-Ukraine Conflict, this paper concluded that the Russia-Ukraine Conflict had a negative impact on the transportation industry in the short term. But in the longer term, the transport index soon leveled off. This paper finds that the global crude oil price index has a significant impact on the transportation industry only in the short term, and the fluctuation is particularly severe in the early stage of the outbreak of Conflict. This paper uses time-series model, VAR and ARMA-GARCH, to capture the impact of this external shock on the yield and volatility of transportation industry. Based on VAR estimation results, this paper finds that the VAR system we use is stationary processes. Further research finds that, through ARMA-GARCH model estimation, the change of international crude oil price will lead to the fluctuation of production of transportation industry. But this effect is delayed, which also reflects the time lag of financial market transmission. In this paper, we find that global crude oil prices have a significant impact on the inventory returns of the transportation industry in the short run. At the beginning of the conflict, returns were volatile, with the magnitude of the oscillations decreasing over time, and while the returns of the transport index were negatively affected by fluctuations in oil prices in the short term, the conflict had little impact on stock returns in the long term.
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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) | 0.001 | 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