{"id":"W2149736324","doi":"10.1016/s0165-1889(99)00087-1","title":"Applications of randomized low discrepancy sequences to the valuation of complex securities","year":2000,"lang":"en","type":"article","venue":"Journal of Economic Dynamics and Control","topic":"Mathematical Approximation and Integration","field":"Mathematics","cited_by":51,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Monte Carlo method; Benchmark (surveying); Valuation (finance); Quasi-Monte Carlo method; Computer science; Statistical hypothesis testing; Algorithm; Unit cube; Mathematical optimization; Mathematics; Applied mathematics; Hybrid Monte Carlo; Econometrics; Statistics; Economics; Finance; Discrete mathematics; Markov chain Monte Carlo","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00115748,0.00006899925,0.0005066978,0.00005836542,0.00003601652,0.00002425487,0.0001173508,0.00002728847,0.0002912931],"category_scores_gemma":[0.0001390843,0.00004125757,0.0001483303,0.00003331705,0.0000898342,0.00008943726,0.000005756424,0.00005541599,0.000002707693],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002951805,"about_ca_system_score_gemma":0.00003842009,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001249039,"about_ca_topic_score_gemma":0.00004600586,"domain_scores_codex":[0.998788,0.0000909843,0.0009102177,0.00005385248,0.00009956866,0.00005736671],"domain_scores_gemma":[0.9984732,0.0006189575,0.00063569,0.0001065464,0.0001311272,0.00003453243],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0008066185,0.00006018132,0.00001309947,0.00007426718,0.0001033574,4.60493e-8,0.0006420483,0.0001802981,0.00004925407,0.9851969,0.00008200618,0.01279192],"study_design_scores_gemma":[0.006905932,0.00005408857,0.00003676639,0.00005987542,0.00009802692,0.000007257908,0.0003465367,0.4110462,0.00004123208,0.5812299,0.0001317789,0.00004246452],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4381917,0.0001648071,0.5496776,0.003870411,0.00005805151,0.001287678,0.00008496464,0.000006229339,0.006658626],"genre_scores_gemma":[0.9925996,0.00009796912,0.006861495,0.00006957351,0.00006195704,0.00003461435,0.000002746005,0.000005049914,0.0002670174],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.554408,"threshold_uncertainty_score":0.3189454,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0203554062319892,"score_gpt":0.2865801979334718,"score_spread":0.2662247917014826,"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."}}