{"id":"W3123509965","doi":"10.1016/j.ijforecast.2014.01.001","title":"Correlation Dynamics and International Diversification Benefits","year":2014,"lang":"en","type":"article","venue":"Open Repository and Bibliography (University of Luxembourg)","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":211,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; University of Toronto","funders":"HEC Montréal; McGill University","keywords":"Diversification (marketing strategy); Portfolio; Economics; Econometrics; Asset allocation; Capital asset pricing model; Financial economics; Portfolio allocation; Asset (computer security); Emerging markets; Business; Finance; Computer science","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001021033,0.0002557756,0.0004743508,0.001102928,0.0004373511,0.002287623,0.0002639128,0.0009130312,0.00963519],"category_scores_gemma":[0.007178954,0.00022086,0.0004205251,0.001293446,0.0008461762,0.002295726,0.001064607,0.001030762,0.0005495926],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007242734,"about_ca_system_score_gemma":0.0003493041,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006111017,"about_ca_topic_score_gemma":0.0005970698,"domain_scores_codex":[0.9997926,0.0000658807,0.000009342097,0.00004361958,0.00003713141,0.00005137663],"domain_scores_gemma":[0.996765,0.001554106,0.0007187488,0.0004288944,0.0002412549,0.0002920483],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002509746,0.00006797139,0.01686937,0.00009819576,0.00009510191,0.0004214377,0.0003382415,0.02177551,0.001606373,0.898005,0.005281717,0.05519007],"study_design_scores_gemma":[0.00006471887,0.0000994916,0.03824361,0.0001100371,0.0001419495,0.0007910787,0.0003385016,0.06335313,0.001089868,0.8862768,0.009450588,0.00004019701],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8370271,0.004778476,0.03655473,0.004278615,0.0000782587,0.00002779977,0.0003453259,0.000186094,0.1167236],"genre_scores_gemma":[0.9928598,0.0008400718,0.001043788,0.00007915193,0.00005381753,0.000007827795,0.00007980545,0.00002074267,0.005014959],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00963519,"threshold_uncertainty_score":0.03223288,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02491735277724628,"score_gpt":0.2004099705380108,"score_spread":0.1754926177607645,"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."}}