TIME-VARYING FREQUENCY CONNECTEDNESS ANALYSIS ACROSS CRUDE OIL, GEOPOLITICAL RISK, ECONOMIC POLICY UNCERTAINTY AND STOCK MARKETS
Bibliographic record
Abstract
As the world is currently in turmoil, geopolitical crises and economic policy uncertainties are increasing significantly. This study aims to provide insight into the dynamics of time–frequency spillovers in the domains of crude oil, geopolitical risk, economic policy uncertainty and stock markets. It represents the first investigation analyzing the time-varying frequency connectedness across the aforementioned domains by adopting the time-varying parameter vector autoregression connectedness combined with the time-varying frequency connectedness measurement [Chatziantoniou et al., 2023]. The study covers the period from January 2004 to February 2023, including the 2008 financial crisis, the COVID-19 pandemic and the turmoil caused by the 2022 Russian–Ukrainian conflict. The analysis finds that short-term frequencies dominate return connectedness, indicating a rapid information processing mechanism responsive to short-run shocks. The stock market indices of oil-exporting countries, the US and the UK act as the primary transmitters of return spillovers. Volatility connectedness is driven by long-term frequencies, with Russia, Canada and the UK serving as the primary volatility spillover transmitters. Economic policy uncertainty is primarily influenced by oil-importing countries. Geopolitical risk mostly serves as the spillover receiver from crude oil, while it primarily transmits spillovers to economic policy uncertainty during major events such as terror attacks, conflicts and wars. The 2022 Russian–Ukrainian conflict amplifies spillovers to economic policy uncertainty. Intriguingly, conflicts deepen economic policy uncertainty, and prior to the conflict, stock market volatility had assimilated the influence of geopolitical risk shocks. The study also employs network topology to visualize spillover transmission mechanisms during the 2022 Russian–Ukrainian conflict.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".