{"id":"W2573751300","doi":"10.3934/qfe.2017.2.125","title":"A Spatial Interpolation Framework for Efficient Valuation of Large Portfolios of Variable Annuities","year":2017,"lang":"en","type":"article","venue":"Quantitative Finance and Economics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Valuation (finance); Portfolio; Key (lock); Monte Carlo method; Interpolation (computer graphics); Forcing (mathematics); Econometrics; Actuarial science; Mathematical optimization; Finance; Economics; Mathematics; Artificial intelligence; Statistics","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":[0.002543194,0.0004907418,0.0007085925,0.0009209039,0.0006626156,0.001122384,0.001529722,0.00110179,0.00362072],"category_scores_gemma":[0.00707919,0.0003785262,0.0009236086,0.00122856,0.001084824,0.001678737,0.001399364,0.001540013,0.0003023962],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001437549,"about_ca_system_score_gemma":0.001673111,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01147022,"about_ca_topic_score_gemma":0.007440741,"domain_scores_codex":[0.9994327,0.0002884638,0.00002574983,0.00006158235,0.0001316357,0.00005982016],"domain_scores_gemma":[0.9980286,0.001287711,0.0001608138,0.0001750425,0.0002561789,0.00009170204],"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":[0.00003240152,0.00002594043,0.0007566499,0.0000304667,0.00001354765,0.00006995678,0.00006153703,0.851304,0.000701067,0.1322586,0.0005327549,0.01421312],"study_design_scores_gemma":[0.000004216738,0.000009168908,0.00007257747,0.000004870378,0.00000200234,0.00001186156,0.000006724577,0.9827676,0.0001140784,0.01643936,0.0005631339,0.000004327477],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01463425,0.0001418018,0.9822279,0.0001869497,0.0000238685,0.0000270885,0.00006552688,0.00008692831,0.002605682],"genre_scores_gemma":[0.4919937,0.0004705985,0.5031949,0.0001175945,0.00008305431,0.0001567953,0.0001860819,0.0001095408,0.003687702],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01147022,"threshold_uncertainty_score":0.02280694,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05169539818158982,"score_gpt":0.3533157264213276,"score_spread":0.3016203282397378,"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."}}