{"id":"W2109784406","doi":"10.1287/mnsc.1060.0575","title":"Efficient Monte Carlo and Quasi–Monte Carlo Option Pricing Under the Variance Gamma Model","year":2006,"lang":"en","type":"article","venue":"Management Science","topic":"Stochastic processes and financial applications","field":"Economics, Econometrics and Finance","cited_by":90,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Killam Trusts; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Monte Carlo method; Variance reduction; Estimator; Importance sampling; Control variates; Variance (accounting); Extrapolation; Quasi-Monte Carlo method; Computer science; Mathematical optimization; Monte Carlo integration; Mathematics; Hybrid Monte Carlo; Markov chain Monte Carlo; Statistics; Economics; Accounting","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":[0.0006340824,0.0001198657,0.0001435248,0.0001522722,0.0005831888,0.000188976,0.0004147166,0.0000271179,0.000002713934],"category_scores_gemma":[0.00001536349,0.0001081169,0.00003340055,0.0007375568,0.0002518773,0.0001350326,0.0001971408,0.00007407448,0.0000395532],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001127002,"about_ca_system_score_gemma":0.00001476596,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006060487,"about_ca_topic_score_gemma":0.00004248492,"domain_scores_codex":[0.9987193,0.000001988931,0.0003108487,0.0005338683,0.0001057652,0.0003282684],"domain_scores_gemma":[0.9993589,0.00001937352,0.0001791008,0.0003640588,0.00003512746,0.00004343489],"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":[0.000001884763,0.00003416102,0.0002030999,0.00001072737,0.000002659687,3.98306e-7,0.00008782021,0.2537506,0.00001283468,0.7453164,0.00003037811,0.000549024],"study_design_scores_gemma":[0.0001578866,0.00001441959,0.03964294,0.000011088,0.000007959979,0.00000116586,0.0001350297,0.8614432,0.000004253494,0.09766487,0.0007700496,0.000147175],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.128402,0.0006321259,0.8594809,0.001054877,0.000121157,0.0003565526,0.00001320977,0.00003324694,0.009905936],"genre_scores_gemma":[0.9938337,0.00004062934,0.004774995,0.00024162,0.00004615088,0.00009736617,4.540091e-7,0.000008974462,0.0009560672],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8654317,"threshold_uncertainty_score":0.4485475,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01777683786036807,"score_gpt":0.2096888750204396,"score_spread":0.1919120371600716,"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."}}