{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002605079,0.0009437932,0.001339914,0.000904857,0.0006017613,0.001590787,0.002015764,0.002428301,0.002214057],"category_scores_gemma":[0.00821564,0.0004651182,0.001139126,0.001490494,0.001024086,0.001262122,0.0009108871,0.002542766,0.0002281764],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001408836,"about_ca_system_score_gemma":0.001086453,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01770697,"about_ca_topic_score_gemma":0.00687161,"domain_scores_codex":[0.9993284,0.0003344042,0.00002946777,0.0001111633,0.0001117363,0.00008494042],"domain_scores_gemma":[0.9966879,0.002096468,0.0004425431,0.0001936509,0.0003793251,0.000199941],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003741347,0.00005388258,0.004220155,0.00004852988,0.00008630251,0.0004165861,0.00009377434,0.8703259,0.0004931415,0.1136962,0.00201304,0.008515189],"study_design_scores_gemma":[0.000007936406,0.00001209599,0.0004228737,0.000004260069,0.00001405656,0.00003713907,0.00001218196,0.9848261,0.00004640968,0.01428047,0.0003249515,0.0000116346],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2647384,0.003025942,0.7139413,0.004009101,0.0004348719,0.000102907,0.000652911,0.0004269955,0.01266751],"genre_scores_gemma":[0.9659416,0.001337073,0.0264264,0.0002169442,0.0002935028,0.00007781776,0.0003271857,0.00004135662,0.005338041],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01770697,"threshold_uncertainty_score":0.03520781,"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."}}