{"id":"W2053079636","doi":"10.1016/j.physa.2011.03.019","title":"Describing<mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" altimg=\"si33.gif\" display=\"inline\" overflow=\"scroll\"><mml:mi>n</mml:mi></mml:math>-day returns with Student’s t-distributions","year":2011,"lang":"en","type":"article","venue":"Physica A Statistical Mechanics and its Applications","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Student's t-distribution; Mathematics; Distribution (mathematics); Volatility (finance); Log-normal distribution; Combinatorics; Inverse; Normal distribution; Statistics; Autoregressive conditional heteroskedasticity; Mathematical analysis; Econometrics; Geometry","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000313735,0.0002226583,0.0002453566,0.00005747107,0.0005336264,0.0001412309,0.0002836837,0.0001696156,0.00004176829],"category_scores_gemma":[0.0001950717,0.0002648617,0.0001182907,0.0002471222,0.00008373811,0.0002289823,0.0001893235,0.0003125409,0.0003644454],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002119948,"about_ca_system_score_gemma":0.00009060247,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002051894,"about_ca_topic_score_gemma":0.00009230117,"domain_scores_codex":[0.9982963,0.00001660012,0.0005369075,0.0005904415,0.0001395748,0.0004202358],"domain_scores_gemma":[0.9986936,0.000197888,0.0003177141,0.0004869516,0.00007118323,0.0002326404],"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":[0.00003860198,0.0002953181,0.00001691977,0.00004992316,0.00007792974,0.000004617803,0.0005537975,0.00003334079,0.00009665207,0.9979101,0.0001140072,0.0008088031],"study_design_scores_gemma":[0.0003221448,0.0002374376,0.0003209018,0.00005198873,0.00009875984,0.000007232391,0.0001732064,0.9113732,0.0003434258,0.08449025,0.002287619,0.0002938673],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5488772,0.0002046048,0.4474843,0.0001607535,0.0001000345,0.0001418077,0.002069077,0.00005699453,0.0009052255],"genre_scores_gemma":[0.9912328,0.000329166,0.007260565,0.0001358987,0.0001558824,0.000485161,0.0003237364,0.00005404628,0.00002273307],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9134198,"threshold_uncertainty_score":0.9999803,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03811773776609086,"score_gpt":0.2456246224324427,"score_spread":0.2075068846663518,"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."}}