{"id":"W4410204326","doi":"10.1016/j.jempfin.2025.101620","title":"The role of macro-finance factors in predicting stock market volatility: A latent threshold dynamic model","year":2025,"lang":"en","type":"article","venue":"Journal of Empirical Finance","topic":"Financial Markets and Investment Strategies","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of New Brunswick; McMaster University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Economics; Volatility (finance); Macro; Stock market; Financial economics; Stock (firearms); Econometrics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001050217,0.0002092283,0.000665529,0.0002441271,0.0001441521,0.00005907645,0.0005139734,0.0001458127,0.00001295034],"category_scores_gemma":[0.0004008172,0.0001660204,0.0002493513,0.0005596928,0.0001641349,0.0003542845,0.0001041961,0.000488943,0.000001735786],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002026901,"about_ca_system_score_gemma":0.0001739048,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004543707,"about_ca_topic_score_gemma":0.00005423478,"domain_scores_codex":[0.9976128,0.00003020115,0.001598399,0.0002748155,0.0001053069,0.0003785045],"domain_scores_gemma":[0.9982349,0.0002290348,0.00107427,0.0003224103,0.0001030707,0.00003633501],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002279194,0.0001849342,0.9075087,0.00003963062,0.00004027157,0.000004827661,0.0002716203,0.00304312,0.00003166023,0.08516526,0.001337395,0.00214461],"study_design_scores_gemma":[0.0003974251,0.00011626,0.509663,0.0001394192,0.000006875874,0.000001564023,0.00005079876,0.2870822,0.00004286661,0.1935975,0.008780706,0.0001213524],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.969166,0.0110227,0.0008022304,0.0009715708,0.0003872004,0.0001935052,0.00005334987,0.000007020075,0.01739647],"genre_scores_gemma":[0.9949932,0.003128875,0.0005185955,0.0001222175,0.00002404889,0.000008526842,8.483582e-7,0.00001356841,0.00119019],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3978457,"threshold_uncertainty_score":0.6770117,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03118596926008011,"score_gpt":0.2699376887778813,"score_spread":0.2387517195178012,"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."}}