{"id":"W1900345999","doi":"10.1016/j.frl.2015.10.004","title":"A DCC-GARCH multi-population mortality model and its applications to pricing catastrophic mortality bonds","year":2015,"lang":"en","type":"article","venue":"Finance research letters","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Actua; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Society of Actuaries","keywords":"Bond; Autoregressive conditional heteroskedasticity; Economics; Mortality rate; Population; Econometrics; Financial economics; Demography; Medicine; Finance; Internal medicine; Volatility (finance)","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":[0.0057647,0.0001944722,0.0002651774,0.0003998556,0.0009915405,0.0002191705,0.0006076939,0.0001016249,0.000004995728],"category_scores_gemma":[0.0004584584,0.000212965,0.00006287162,0.001553914,0.0004069273,0.0005353761,0.0003085121,0.0004723281,0.00005739143],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004408841,"about_ca_system_score_gemma":0.000209358,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0153721,"about_ca_topic_score_gemma":0.007088918,"domain_scores_codex":[0.9953796,0.0005712493,0.0004006459,0.0007600687,0.001836804,0.001051652],"domain_scores_gemma":[0.9983094,0.00009487094,0.0001082363,0.0006549557,0.0004447718,0.0003878021],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006989487,0.0005831262,0.8472638,0.0002720303,0.0001371976,0.00004973396,0.01456514,0.04555536,0.002173569,0.06811615,0.01121319,0.01000087],"study_design_scores_gemma":[0.0005873501,0.00005750949,0.9541925,0.00006308931,0.0000286588,6.286626e-7,0.000786405,0.02741591,0.00004057836,0.002896265,0.01346843,0.0004627239],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.98047,0.0002415593,0.01150193,0.004282919,0.00008234166,0.002171789,0.00008535054,0.000104518,0.001059612],"genre_scores_gemma":[0.9949889,0.0002113428,0.00295077,0.000424085,0.0002099866,0.0008715745,0.00004637946,0.00002499741,0.0002719323],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1069287,"threshold_uncertainty_score":0.9911846,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.23000517011553,"score_gpt":0.4558035660001222,"score_spread":0.2257983958845922,"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."}}