{"id":"W1976830408","doi":"10.1556/aoecon.61.2011.1.3","title":"Re-examining covariance risk dynamics in international stock markets using quantile regression analysis","year":2011,"lang":"en","type":"article","venue":"Acta Oeconomica","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Econometrics; Quantile regression; Economics; Stock (firearms); Quantile; Covariance; Autoregressive conditional heteroskedasticity; Financial economics; Volatility (finance); Markov chain; BETA (programming language); Stock market; Mathematics; Statistics; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009558413,0.0001994038,0.0005549608,0.0006742394,0.0001150577,0.00006163315,0.0004157977,0.0001722707,0.0009075272],"category_scores_gemma":[0.0002327859,0.0002477071,0.0001687803,0.0003466522,0.00004561598,0.000510524,0.0001379433,0.0002802215,0.00007478525],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005567729,"about_ca_system_score_gemma":0.000032606,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003740067,"about_ca_topic_score_gemma":0.002311903,"domain_scores_codex":[0.9980261,0.00003546022,0.00094589,0.0006331358,0.00003130821,0.0003281195],"domain_scores_gemma":[0.9986272,0.00008023944,0.0007164006,0.0004765679,0.00002779463,0.00007173313],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001237937,0.00009067028,0.9894528,0.00000605665,0.000176071,0.000003369223,0.0007526687,0.001037271,0.000007405711,0.006354322,0.0000395106,0.00195611],"study_design_scores_gemma":[0.0002948664,0.00001627687,0.3465987,0.00001573115,0.00002574075,4.449757e-7,0.000170927,0.6486912,0.00001116185,0.003472948,0.0004976392,0.0002043061],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9653004,0.0001738226,0.01887637,0.00006042506,0.0005367594,0.0001306274,0.0002297717,0.00002949388,0.01466227],"genre_scores_gemma":[0.9875791,0.0002654999,0.01180921,0.00005193009,0.00006726421,0.000009586,0.00004699185,0.00002746704,0.000142965],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6476539,"threshold_uncertainty_score":0.9999975,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08221072423608618,"score_gpt":0.2684189439907287,"score_spread":0.1862082197546426,"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."}}