{"id":"W4413845651","doi":"10.1515/econ-2025-0163","title":"Impact of External Shocks on Global Major Stock Market Interdependence: Insights from Vine-Copula Modeling","year":2025,"lang":"en","type":"article","venue":"Economics","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"National Natural Science Foundation of China","keywords":"Vine copula; Copula (linguistics); Economics; Econometrics; Vine; Stock market; Stock (firearms); Tail dependence; Financial economics; Geography; Mathematics; Statistics; Biology; Multivariate statistics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00029514,0.0002666838,0.0006616958,0.0002001036,0.00007387937,0.0000935218,0.0004890775,0.0001956665,0.001055227],"category_scores_gemma":[0.00008253945,0.0003004504,0.0003582718,0.0001390722,0.00004198786,0.0002163391,0.0001891442,0.0002171597,0.00003385048],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007153871,"about_ca_system_score_gemma":0.00009100718,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002782849,"about_ca_topic_score_gemma":0.0004980678,"domain_scores_codex":[0.9980442,0.00002395528,0.001013304,0.0006134785,0.00002642383,0.0002786379],"domain_scores_gemma":[0.998744,0.00008327878,0.0003812763,0.0006536139,0.00003761932,0.0001002061],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005406246,0.0002213216,0.9298443,0.00003248801,0.0002780326,0.000002319615,0.00008814378,0.007264372,0.000009882512,0.05890824,0.0005314951,0.002278783],"study_design_scores_gemma":[0.0005661929,0.00006447422,0.1109504,0.0000364538,0.000008768196,5.302482e-7,0.00001103096,0.7164126,0.000005128601,0.1716335,0.0001186887,0.0001922596],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.910063,0.0005297693,0.02258286,0.00004883954,0.0006945934,0.0001983942,0.001102933,0.000022303,0.06475729],"genre_scores_gemma":[0.9982213,0.0001303444,0.0007510722,0.0001065474,0.00008497418,0.00001128598,0.00003366686,0.00001789747,0.0006428451],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8188939,"threshold_uncertainty_score":0.9999447,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01818810258046792,"score_gpt":0.2585512506170914,"score_spread":0.2403631480366235,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}