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Record W4413845651 · doi:10.1515/econ-2025-0163

Impact of External Shocks on Global Major Stock Market Interdependence: Insights from Vine-Copula Modeling

2025· article· en· W4413845651 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEconomics · 2025
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMarket Dynamics and Volatility
Canadian institutionsMcMaster University
FundersNational Natural Science Foundation of China
KeywordsVine copulaCopula (linguistics)EconomicsEconometricsVineStock marketStock (firearms)Tail dependenceFinancial economicsGeographyMathematicsStatisticsBiologyMultivariate statistics

Abstract

fetched live from OpenAlex

Abstract This article investigates the dynamic changes in the interdependence structure and strength among ten financially significant stock markets across Asia, Europe, and the USA, in the context of recent global public health events and regional conflicts. Employing the Vine-Copula model, our analysis reveals that major events exert varying impacts on the interdependencies across different regions. The COVID-19 pandemic shifted European markets from a symmetric dependence structure to an asymmetric structure that is more sensitive to negative news. Conversely, the impact on Asian markets is the opposite, and the interdependence between China’s stock market and other major markets shows a decreasing trend. The Russia–Ukraine conflict has had minimal impact on stock markets, excluding Russia. Moreover, stock markets exhibit stronger co-movements during market downturns. Our research provides new insights into how global events impact stock market interdependencies and underscores the importance of region-specific strategies in managing financial risks and maintaining market stability.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.819
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.018
GPT teacher head0.259
Teacher spread0.240 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it