Financial Integration and Comovements Between Capital Markets and Oil Markets
Bibliographic record
Abstract
This chapter aims to test the financial integration and movements in the capital markets of Germany (DAX), USA (Dow Jones), France (CAC 40), UK (FTSE 100), Italy (FTSE MIB), Russia (MOEX), Japan (NIKKEI 225), and Canada (S&P TSX), China (SHANGHAI and SHENZHEN); as well as the oil markets of the US (AMERICAS-DS OIL), Asia (ASIA-DS OIL), Canada (CANADA-DS OIL), the Emirates (EMU-DS OIL), China (CHINA-DS OIL), Nigeria (NIGERIA-DS OIL), and the United Kingdom (UK-DS OIL) over the period January 1, 2020 to May 6, 2022. The results suggest that long-term relationships between capital markets and oil markets do not help explain short-term moves. The authors consider the results achieved to be of interest to investors seeking opportunities in these financial markets, and also to policymakers to undertake institutional reforms to increase market efficiency and promote sustainable growth in financial markets.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".