{"id":"W1807529681","doi":"10.1080/00036846.2015.1049338","title":"Modelling conditional moments and correlation with the continuous hidden-threshold-skew-normal distribution","year":2015,"lang":"en","type":"article","venue":"Applied Economics","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Volatility clustering; Econometrics; Kurtosis; Autoregressive conditional heteroskedasticity; Conditional probability distribution; Stylized fact; Conditional variance; Heteroscedasticity; Volatility (finance); Economics; Financial models with long-tailed distributions and volatility clustering; Skewness; Conditional expectation; Autoregressive model; Mathematics; Statistics; Stochastic volatility; Forward volatility","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":[0.0004677117,0.0001566372,0.0002682964,0.00004826296,0.000205088,0.0001081476,0.0001205737,0.0001455657,0.00001624442],"category_scores_gemma":[0.000009013379,0.0001516744,0.00003535923,0.00006882343,0.0001011045,0.0002645501,0.00005227398,0.000230544,0.0001180266],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001418381,"about_ca_system_score_gemma":0.00003621107,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001129778,"about_ca_topic_score_gemma":0.00001957177,"domain_scores_codex":[0.9989666,0.000004487848,0.0004057474,0.0003470356,0.00003073976,0.0002453717],"domain_scores_gemma":[0.9993615,0.00003701962,0.000264413,0.0002048054,0.00003375028,0.00009850642],"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":[0.00009872983,0.00003167621,0.01977446,0.000005253896,0.00003035625,3.272878e-7,0.0004815387,0.2544655,5.242169e-7,0.7240281,0.0004997683,0.0005838377],"study_design_scores_gemma":[0.001195322,0.00005107971,0.004278232,0.000004319484,0.00001289471,0.000005405582,0.0002803695,0.7926229,0.00001378754,0.1907443,0.01050582,0.0002855645],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.699938,0.0002239684,0.2941082,0.0002811624,0.0001027938,0.0002119957,0.0002011558,0.00002440222,0.004908398],"genre_scores_gemma":[0.9980331,0.00008639332,0.001018478,0.0001609089,0.0001237365,0.00004337453,0.0003746681,0.00002045412,0.0001388535],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5381575,"threshold_uncertainty_score":0.6185101,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03113512768823336,"score_gpt":0.1911372156875616,"score_spread":0.1600020879993282,"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."}}