{"id":"W4408022963","doi":"10.2139/ssrn.5158101","title":"Hedging universal life insurance policies","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University; Université Laval","funders":"","keywords":"Life insurance; Actuarial science; Business; Economics","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":[0.002393453,0.0005172353,0.001124433,0.0007467723,0.0003148186,0.002257869,0.0007299039,0.002073909,0.007247796],"category_scores_gemma":[0.01180874,0.0003823728,0.0005332512,0.0008309068,0.0009599494,0.001975539,0.00136465,0.001933408,0.0003850585],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001527801,"about_ca_system_score_gemma":0.0009392541,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004141314,"about_ca_topic_score_gemma":0.003034147,"domain_scores_codex":[0.9993801,0.0002026096,0.0000330065,0.0001163373,0.0001003126,0.0001675876],"domain_scores_gemma":[0.9966093,0.002069803,0.0005730432,0.0003427303,0.0001378384,0.0002673512],"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":[0.0007017974,0.0005860841,0.05609817,0.0004061944,0.0003780732,0.000788669,0.0009160674,0.3729268,0.003076824,0.4230553,0.009477291,0.1315887],"study_design_scores_gemma":[0.000069544,0.0002700356,0.02371495,0.0001010694,0.0001143557,0.0001815255,0.0004799536,0.4576155,0.0008895166,0.5133902,0.003129323,0.00004406574],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.92756,0.002163,0.05092999,0.004568038,0.0002135056,0.0000418589,0.0007732206,0.0002829434,0.0134674],"genre_scores_gemma":[0.9927188,0.0006382494,0.0009852191,0.00008477531,0.00005512248,0.000008873981,0.0001390052,0.00001173435,0.005358094],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007247796,"threshold_uncertainty_score":0.02424634,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01443820914347536,"score_gpt":0.3002082844990131,"score_spread":0.2857700753555377,"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."}}