The Impacts of the Russia–Ukraine Invasion on Global Markets and Commodities: A Dynamic Connectedness among G7 and BRIC Markets
Bibliographic record
Abstract
The conflict between Russia and Ukraine has been causing knock-on effects worldwide. The supply and price of major commodity markets (oil, gas, platinum, gold, and silver) have been greatly impacted. Due to the ongoing conflict, financial markets across the world have experienced a strong dynamic regarding commodities prices. This effect can be considered the biggest change since the occurrence of the financial crisis in the year 2008, which explicitly influenced the oil and gold markets. This study attempts to investigate the impacts of the Russian invasion crisis on the dynamic connectedness among five commodities and the G7 and BRIC (leading stock) markets. We have applied the time-varying parameter vector autoregressive (TVP-VAR) method, which reflects the way spillovers are shaped by various crises periods, and we found extreme connectedness among all commodities and markets (G7 and BRIC). The findings show that gold and silver (commodities) and the United States, Canada, China, and Brazil (stock markets) are the receivers from the rest of the commodities/market’s transmitters of shocks during this invasion crisis. This research has policy implications that could be beneficial to commodity and stock investors, and these implications could guide them to make many decisions about investment in such tumultuous situations. Policymakers, institutional investors, bankers, and international organizations are the possible beneficiaries of these policy decisions.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".