{"id":"W2141448373","doi":"10.5555/2433508.2433844","title":"American option pricing with randomized quasi-Monte Carlo simulations","year":2010,"lang":"en","type":"article","venue":"Winter Simulation Conference","topic":"Mathematical Approximation and Integration","field":"Mathematics","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Variance reduction; Monte Carlo method; Control variates; Monte Carlo methods for option pricing; Variance (accounting); Importance sampling; Quasi-Monte Carlo method; Valuation of options; Computer science; Monte Carlo integration; Econometrics; Mathematics; Markov chain Monte Carlo; Statistics; Hybrid Monte Carlo; 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":[0.01112374,0.0005895984,0.001178281,0.0008605402,0.0004643243,0.001430829,0.001629874,0.001166234,0.002336973],"category_scores_gemma":[0.03568549,0.0006266844,0.0007186976,0.00105108,0.00152206,0.00168667,0.0008594618,0.001499356,0.0002166729],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001028974,"about_ca_system_score_gemma":0.001534301,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008434808,"about_ca_topic_score_gemma":0.005909374,"domain_scores_codex":[0.9916865,0.00704408,0.0001350494,0.0002151261,0.0007148307,0.0002044019],"domain_scores_gemma":[0.9561052,0.03754786,0.001763159,0.002129285,0.002083921,0.0003705447],"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":[0.0001554929,0.00008667783,0.000637462,0.00003867415,0.00005874124,0.00002898837,0.00002563861,0.9524658,0.0003784099,0.04065051,0.0003114083,0.005162233],"study_design_scores_gemma":[0.00001089905,0.00001300261,0.0000362133,0.000001688406,0.000003091985,0.000002312413,0.000001716204,0.9974396,0.00007677258,0.002366081,0.00004585083,0.000002754594],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1083574,0.000411241,0.8874539,0.0004786328,0.00008617342,0.00009485606,0.00005171596,0.0004079951,0.002658068],"genre_scores_gemma":[0.7798645,0.0001732662,0.2181958,0.0001520457,0.00007128591,0.0002336417,0.00008405447,0.00009587427,0.001129495],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01112374,"threshold_uncertainty_score":0.05882859,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0429581473054977,"score_gpt":0.3370594250824946,"score_spread":0.2941012777769969,"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."}}