Revisiting the currency-commodity nexus: New insights into the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"> <mml:mrow> <mml:msup> <mml:mi mathvariant="bold-italic">R</mml:mi> <mml:mn mathvariant="bold">2</mml:mn> </mml:msup> </mml:mrow> </mml:math> decomposed connectedness and the role of global shocks
Bibliographic record
Abstract
In this study, we incorporate the novel R 2 decomposed connectedness and event-driven statistical analysis to empirically investigate the dynamic return and volatility connectedness of six leading currencies and various commodity markets, and further provide formal statistical evidence of how global shocks can trigger significant increases in the currency-commodity connectedness. With effective differentiation between contemporaneous correlations and lagged spillovers , the empirical results show that, while the overall connectedness is mainly driven by contemporaneous components during tranquil periods, the lagged volatility spillovers play a more prominent role especially during extreme market turmoil. Moreover, both return and volatility transmission present significant time-varying characteristics and even-dependent patterns, with prominent spikes during periods of extreme events such as the 2007–2009 global financial crisis and 2020 COVID-19 pandemic, which is further supported with formal statistical evidence utilizing the event-driven probabilistic analysis. Lastly, we further spot that the commodity currencies such as the Canadian dollar and Australian dollar prevailingly transmit to the connectedness network, while the agricultural commodity markets mainly serve as risk receivers, with potential net position reversal under various market conditions. Overall, our analysis provides valuable insights into the intricacies of currency-commodity nexus which are highly conducive to a better understanding of the potential risk contagion among these markets and corresponding risk management for policy makers and investors.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".