Crude oil, forex, and stock markets: unveiling the higher-order moment and cross-moment risk spillovers in times of turmoil
Bibliographic record
Abstract
This study employs an analytical framework that integrates realized moment measures with a TVP-VAR-based extended joint connectedness approach to examine higher-order moment and cross-moment risk spillovers among crude oil futures (CL), Dollar Index futures (DX), and S&P 500 E-mini futures (ES). The findings reveal that the interconnectedness between crude oil, stock, and forex markets is shaped by distributional moments, with realized volatility (RV) spillovers being significantly stronger than those of higher-order moments (RS, RK) and jumps (RJ). Crude oil consistently acts as a net transmitter across all measures, underscoring its dominant role, while the forex and stock markets emerge as the primary net recipients of volatility and kurtosis spillovers, respectively. Spillover dynamics exhibit time-varying behavior and high sensitivity to crises, including the crude oil price collapse, the US‒China trade war, the COVID-19 pandemic, and the ongoing conflicts in Ukraine and the Middle East. Notably, the RV, RJ, and cross-moment joint spillovers react more sharply to health crises, whereas higher-order moments respond more strongly to geopolitical shocks.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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 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".