A Study of the Dynamic Relationship between Crude Oil Price and the Steel Price Index
Bibliographic record
Abstract
Bulk shipping providers predominantly supply transportation services for bulk cargo, such as iron ore, grain and coal. As steel price index is a leading indicator of Baltic Dry Index, the cost of marine fuel becomes one of the key costs for shipping providers. By collecting data and building a VARMA model, this study will attempt to discover the dynamic relationship between crude oil price and the global steel price index. The results of this study are as follows: (1) The outcomes of examining the unit root using the Phillips-Perron-test indicates that the two variables, the crude oil price and global steel price index, have a co-integration effect. This also proves that a long term balancing phenomenon exists between the crude oil price and global steel price index. (2) VARMA (3, 2) is the most suitable stage of the model for both the crude oil price and global steel price index. (3) There is a unidirectional relationship between crude oil price and the global steel price index, which means that the price of crude oil is only impacted from its own volatility. However, the global steel price index is impacted from both the movements of its own price and the volatility of crude oil price. (4) The crude oil price moves prior to movements in the global steel price index. When crude oil price increases, the global steel price index follows this upward movement. This study aims to provide a reference for investors¡¯ investment activities and shipping operators¡¯ risk aversion decisions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".