{"id":"W4416858206","doi":"10.1080/14697688.2025.2565290","title":"Modeling ex post variance jumps: implications for density and tail risk forecasting","year":2025,"lang":"en","type":"article","venue":"Quantitative Finance","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Tail risk; Variance (accounting); Ex-ante; Tail dependence; Measure (data warehouse)","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":[0.000733236,0.0001910854,0.0004296457,0.0001783599,0.0005506024,0.00007246864,0.0001801521,0.0001134765,0.000003345494],"category_scores_gemma":[0.001724761,0.0002376202,0.0001120933,0.0003611705,0.00008174107,0.0003353298,0.00007573582,0.000191821,0.00002024055],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007079315,"about_ca_system_score_gemma":0.00005708235,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006667585,"about_ca_topic_score_gemma":0.0002667162,"domain_scores_codex":[0.9983355,0.00001902537,0.0006260062,0.0006534361,0.00002110748,0.0003448712],"domain_scores_gemma":[0.9987887,0.0003186411,0.0002845477,0.000322243,0.000250362,0.00003555216],"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":[0.00006415174,0.00004197368,0.04605458,0.00006898741,0.00002651525,2.554292e-7,0.000515183,0.009371057,0.00003889337,0.9395825,0.0001211468,0.004114795],"study_design_scores_gemma":[0.0003413138,0.00005898732,0.03879939,0.00006031642,0.00001099578,5.761277e-7,0.00006262453,0.6644551,0.00001800356,0.2941311,0.001876543,0.0001850805],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4485409,0.002669737,0.5466024,0.0005324654,0.0001434919,0.0002871991,0.0003745811,0.00002637159,0.0008227884],"genre_scores_gemma":[0.9300761,0.0008250237,0.06835797,0.0001773991,0.00003443211,0.00008959203,0.00002235407,0.00001908015,0.0003980177],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.655084,"threshold_uncertainty_score":0.9689872,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08986095564712096,"score_gpt":0.2927226605799882,"score_spread":0.2028617049328673,"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."}}