Risk transmission and interconnectedness between Fintech and oil-exporting markets during global crises
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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. • Examines Fintech–oil market interconnectedness during COVID-19 and Russia–Ukraine war. • Applies a TVP-VAR framework to uncover dynamic spillovers among key oil-exporting markets. • Finds Canada, Russia, and Iraq as dominant risk transmitters in the COVID-19 period. • Iraq remains a persistent transmitter, while U.S., Russia, and Canada show self-links in Russia–Ukraine war. • Fintech and technological innovation absorb volatility, revealing fragility and systemic relevance.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it