{"id":"W2898328045","doi":"10.1002/ijfe.1679","title":"Tail dependence networks of global stock markets","year":2018,"lang":"en","type":"article","venue":"International Journal of Finance & Economics","topic":"Complex Systems and Time Series Analysis","field":"Economics, Econometrics and Finance","cited_by":88,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Tail dependence; Pearson product-moment correlation coefficient; Copula (linguistics); Econometrics; Stock (firearms); Correlation coefficient; Complex network; Financial market; Stock market; Economics; Correlation; Financial economics; Statistics; Mathematics; Finance; Combinatorics; Geography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006977079,0.0002431788,0.0003471875,0.003163422,0.0006758058,0.000890889,0.000483079,0.0006222937,0.002619862],"category_scores_gemma":[0.005749628,0.0003013165,0.0004490555,0.001636091,0.0007448075,0.001602833,0.0007932213,0.0003916475,0.0002079874],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006486093,"about_ca_system_score_gemma":0.0003014512,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003637134,"about_ca_topic_score_gemma":0.003495238,"domain_scores_codex":[0.9995621,0.0001681607,0.00001741154,0.000115717,0.00008333989,0.00005334058],"domain_scores_gemma":[0.9962654,0.001779162,0.0008526706,0.0002735488,0.0005527008,0.0002764826],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004214685,0.000185709,0.1229338,0.0003282804,0.0002981405,0.001161655,0.001602442,0.4015911,0.01685026,0.3551013,0.006772436,0.09275349],"study_design_scores_gemma":[0.00002609062,0.00005973705,0.0441795,0.0000505383,0.00007996095,0.0004447958,0.0004152792,0.7801636,0.002231685,0.1682466,0.004053646,0.00004870317],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8417291,0.0003484786,0.150976,0.0003028154,0.00001054099,0.00005140922,0.0005766589,0.0001295841,0.005875361],"genre_scores_gemma":[0.9903938,0.0001690421,0.00783535,0.00003706029,0.00001212308,0.00002964368,0.0004081229,0.00001454583,0.001100195],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003637134,"threshold_uncertainty_score":0.008764327,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01830202449893291,"score_gpt":0.2327255041566016,"score_spread":0.2144234796576687,"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."}}