{"id":"W2737767256","doi":"","title":"Pricing and Hedging GMWB Riders in a Binomial Framework","year":2012,"lang":"en","type":"preprint","venue":"Spectrum Research Repository (Concordia University)","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Binomial options pricing model; Diversification (marketing strategy); Toolbox; Binomial (polynomial); Asset (computer security); Binomial distribution; Valuation of options; Econometrics; Trinomial tree; Black–Scholes model; Computer science; Actuarial science; Economics; Mathematics; Business; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.003958207,0.0003729183,0.0005697071,0.002544017,0.001705653,0.0005287695,0.001276246,0.000687264,0.00005725719],"category_scores_gemma":[0.000377911,0.0004730559,0.0002508029,0.001897723,0.001779901,0.0005307692,0.002051627,0.003156552,0.00002121027],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001989748,"about_ca_system_score_gemma":0.001108209,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1841306,"about_ca_topic_score_gemma":0.06517252,"domain_scores_codex":[0.9931067,0.002274739,0.0003957038,0.001117167,0.001380113,0.001725616],"domain_scores_gemma":[0.9974176,0.0007760432,0.0002595038,0.0008480236,0.0001461651,0.0005527103],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001348985,0.000134291,0.9377756,0.0002345709,0.0001653416,0.000867827,0.009173921,0.00007531607,0.00004321913,0.04942741,0.0002383989,0.001729186],"study_design_scores_gemma":[0.0008728596,0.0001005209,0.868726,0.0007642511,0.0001289058,0.000005734003,0.02407933,0.000166814,0.0002444655,0.01968828,0.08401734,0.001205517],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7990892,0.0005248223,0.000191982,0.0009122746,0.001723495,0.001153565,0.00000561275,0.0001522737,0.1962468],"genre_scores_gemma":[0.9942942,0.002034395,0.0001914836,0.00002157983,0.001217058,0.00001106927,0.000004533343,0.00004224926,0.002183492],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1952049,"threshold_uncertainty_score":0.9997721,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04374396169061167,"score_gpt":0.3234768796507879,"score_spread":0.2797329179601762,"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."}}