Tail-risk spillovers and interconnectedness in international logistics markets: a QVAR approach
Bibliographic record
Abstract
This research explores the interdependence within the international logistics sector among 17 nations, utilizing a quantile-based technique to assess the transmission of returns. By analyzing daily data from DataStream spanning from 1 June 2016, to 12 August 2024, we apply the Quantile Vector Autoregression framework to examine the synchronous behavior of variables, considering the magnitude of shocks. Our findings reveal varying degrees of linkage at the lower, median, and upper quantiles of the conditional distribution. The results show that extreme events, such as the COVID-19 pandemic and the Russia-Ukraine war, significantly amplified spillovers across logistics markets, while the impact of the Israel-Hamas conflict was more regionally contained. Regional clustering and geographical proximity play a crucial role, with stronger interconnections observed among neighboring countries, such as the US and Canada, and Germany and France. The US stands out as a dominant transmitter of shocks, while countries in Asia and Oceania tend to be net receivers, highlighting their vulnerability to external disruptions. These results underscore the need for quantile-based risk assessments in regulatory frameworks and risk management strategies to better manage asymmetric risk transmissions during global crises.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".