{"id":"W1758794879","doi":"10.1007/978-3-642-56046-0_12","title":"Pricing American Derivatives using Simulation: A Biased Low Approach","year":2002,"lang":"en","type":"book-chapter","venue":"","topic":"Mathematical Approximation and Integration","field":"Mathematics","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Control variates; Variance reduction; Estimator; Computer science; Valuation (finance); Monte Carlo method; Quasi-Monte Carlo method; Variance (accounting); Exploit; Importance sampling; Mathematical optimization; Rate of convergence; Algorithm; Applied mathematics; Mathematics; Markov chain Monte Carlo; Hybrid Monte Carlo; Statistics; Finance; Key (lock); Economics; Accounting","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":[0.002513598,0.000804307,0.001651811,0.001316024,0.0008155152,0.003234867,0.001810004,0.001398803,0.005658331],"category_scores_gemma":[0.01344561,0.0006316096,0.0009175503,0.001469472,0.001765267,0.004176693,0.00173519,0.003089284,0.0008717884],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001520628,"about_ca_system_score_gemma":0.0009618381,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002036826,"about_ca_topic_score_gemma":0.001645005,"domain_scores_codex":[0.9986928,0.0007352841,0.00003866829,0.0000796379,0.0003801523,0.00007343195],"domain_scores_gemma":[0.9946131,0.003898448,0.0001715285,0.0006132139,0.000578374,0.0001254214],"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":[0.00004060189,0.00003545239,0.0004972289,0.00004232717,0.00003307825,0.00005159577,0.00005198373,0.1003126,0.0005670458,0.8693625,0.002560915,0.0264446],"study_design_scores_gemma":[0.00000781931,0.00001022969,0.00007534462,0.0000130868,0.0000108596,0.00002802519,0.000006938355,0.5972234,0.000224189,0.4006676,0.001723694,0.000008796804],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01224421,0.00090126,0.9579561,0.001227456,0.0001135206,0.0000249881,0.00004634573,0.0002432617,0.02724275],"genre_scores_gemma":[0.6896801,0.002872504,0.2676128,0.0008631913,0.0006735542,0.000176449,0.0001662432,0.0005176879,0.03743763],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005658331,"threshold_uncertainty_score":0.018929,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1714898502424862,"score_gpt":0.3404372164880551,"score_spread":0.1689473662455689,"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."}}