{"id":"W2951542203","doi":"10.22606/jaam.2019.43002","title":"Geometric Brownian Motion Assumption and Generalized Hyperbolic Distribution on Modeling Returns","year":2019,"lang":"en","type":"article","venue":"Journal of Advances in Applied Mathematics","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Bank of Canada","funders":"","keywords":"Mathematics; Variance-gamma distribution; Geometric Brownian motion; Goodness of fit; Econometrics; Distribution (mathematics); Brownian motion; Kernel (algebra); Normal distribution; Empirical distribution function; Statistics; Applied mathematics; Mathematical analysis; Diffusion process; Economics; Asymptotic distribution; Pure mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.0008754588,0.0001297036,0.0004764537,0.0003680425,0.00003981924,0.00003770039,0.0001135554,0.0001012247,0.00001889374],"category_scores_gemma":[0.0001416172,0.000129687,0.00007043596,0.0003170878,0.00001594065,0.000294599,0.00002349808,0.000258997,0.00003065761],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000148918,"about_ca_system_score_gemma":0.000009788247,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004279132,"about_ca_topic_score_gemma":0.000003508474,"domain_scores_codex":[0.9986056,0.00000646032,0.0009407139,0.0001782402,0.00009135847,0.0001775939],"domain_scores_gemma":[0.9990215,0.00006571868,0.0006669302,0.0001570361,0.00004078115,0.0000480812],"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":[0.0001984322,0.0004705808,0.007805705,0.0005150028,0.00002967689,0.000002968391,0.001133893,0.3861942,0.0004242364,0.5779132,0.00002036751,0.02529181],"study_design_scores_gemma":[0.001239868,0.0001159388,0.0008979289,0.0001332783,0.000009289114,0.00001084601,0.0001680709,0.6441821,0.0001291134,0.3519728,0.0009285352,0.0002121156],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7495732,0.002121096,0.2460003,0.000043008,0.0002057798,0.0001555599,0.00001501176,0.000007173803,0.001878779],"genre_scores_gemma":[0.974611,0.003736384,0.02149425,0.00002542003,0.000090476,0.000003538881,0.000006495166,0.00001524229,0.00001716365],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.257988,"threshold_uncertainty_score":0.5288484,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02464199769833934,"score_gpt":0.2383240018889759,"score_spread":0.2136820041906365,"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."}}