{"id":"W3123605808","doi":"","title":"Multivariate mixed normal conditional heteroskedasticity","year":2006,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Center for Interuniversity Research and Analysis on Organizations; HEC Montréal","funders":"","keywords":"Mathematics; Covariance matrix; Conditional variance; Gibbs sampling; Estimation of covariance matrices; Covariance; Econometrics; Multivariate statistics; Heteroscedasticity; Inverse-Wishart distribution; Multivariate normal distribution; Statistics; Conditional probability distribution; Applied mathematics; Volatility (finance); Autoregressive conditional heteroskedasticity; Bayesian probability","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":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.002606635,0.0004597604,0.001071614,0.0008849617,0.0002914848,0.0002463242,0.0007993304,0.0008164862,0.000235725],"category_scores_gemma":[0.0006956912,0.000628586,0.0003865325,0.0001667836,0.0003432908,0.0002325301,0.001148062,0.002315186,0.0001646161],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001178836,"about_ca_system_score_gemma":0.0002925719,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002429009,"about_ca_topic_score_gemma":0.0009595705,"domain_scores_codex":[0.9953334,0.000123863,0.001746242,0.001515435,0.0001251954,0.001155869],"domain_scores_gemma":[0.9977569,0.0004041932,0.0005446337,0.0009699665,0.0001232634,0.0002010522],"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":[0.0005143327,0.00177365,0.3746972,0.0008572992,0.0003805303,0.000107609,0.0009526676,0.4279021,0.00007930477,0.1219804,0.0009096665,0.06984527],"study_design_scores_gemma":[0.001552883,0.0001022324,0.317179,0.0001605417,0.000007317153,0.000005242255,0.00005337606,0.4951907,0.00006058926,0.1636268,0.02088731,0.00117403],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9044574,0.0003862496,0.002270712,0.0002296367,0.001346637,0.0008486424,0.002363278,0.00007779832,0.08801962],"genre_scores_gemma":[0.9935443,0.0009049254,0.002592828,0.00006467914,0.0005296061,0.0002270979,0.000664536,0.00008828435,0.001383721],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0890869,"threshold_uncertainty_score":0.9999865,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06098703222832499,"score_gpt":0.3013660132485436,"score_spread":0.2403789810202186,"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."}}