Risk transmission and interconnectedness between Fintech and oil-exporting markets during global crises
Bibliographic record
Abstract
This study explores the dynamic interconnectedness and risk spillovers between FinTech, technological innovation, and the stock markets of major oil-exporting economies, i.e. Saudi Arabia, Russia, the United States, Iraq, and Canada, during the COVID-19 pandemic and the Russia–Ukraine war. Using a Time-Varying Parameter Vector Autoregression (TVP-VAR) framework, we uncover how systemic linkages evolve across crises. Results show that during COVID-19, Canada, Russia, and Iraq acted as dominant shock transmitters, while the United States, Saudi Arabia, and technological innovation were net recipients, reflecting structural vulnerabilities tied to market depth, institutional strength, and reliance on resource revenues. In contrast, during the Russia–Ukraine war, Iraq remained a persistent transmitter, while the U.S., Russia, and Canada exhibited greater self-connectedness, signaling inward market adjustments. FinTech and technological innovation consistently absorbed volatility, highlighting their growing systemic relevance but also fragility under global uncertainty. The findings highlight shifting contagion channels, urging market participants to adjust hedging strategies and policymakers to strengthen macro prudential coordination for stability.
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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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".