{"id":"W4382601985","doi":"10.5539/ijef.v15n8p27","title":"Variance Risk Premium Components in Japan for Predictability: Evidence from the COVID-19 Pandemic","year":2023,"lang":"en","type":"article","venue":"International Journal of Economics and Finance","topic":"Credit Risk and Financial Regulations","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Japan Society for the Promotion of Science","keywords":"Predictability; Coronavirus disease 2019 (COVID-19); Economics; Variance risk premium; Pandemic; Econometrics; Predictive power; Variance (accounting); Asset (computer security); Volatility (finance); Financial economics; Statistics; Mathematics; Stochastic volatility; Medicine; Internal medicine; Computer science; Volatility risk premium","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001538992,0.0001176249,0.0003225149,0.0001801494,0.0001123133,0.00009595563,0.0005362774,0.00008548805,0.00001534329],"category_scores_gemma":[0.001628832,0.0001137387,0.0001335622,0.0001337387,0.0001051388,0.0003817911,0.00009198242,0.0002014352,0.00001574],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002421531,"about_ca_system_score_gemma":0.0001038725,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009540142,"about_ca_topic_score_gemma":0.0006589045,"domain_scores_codex":[0.9985452,0.00002044534,0.0009327002,0.0002804091,0.00003998824,0.0001812298],"domain_scores_gemma":[0.9976187,0.001088419,0.0009578546,0.0001890063,0.0000850479,0.00006095898],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001682365,0.00003490595,0.9343526,0.000004330589,0.00005355455,0.000002612791,0.0008936804,0.0190607,0.00000520234,0.04055608,0.001052905,0.003815162],"study_design_scores_gemma":[0.0008889526,0.00004699639,0.7185086,0.00003955782,0.000006586692,0.00001227965,0.00004714795,0.04276963,0.000002227976,0.1287399,0.1088267,0.0001114178],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9852714,0.002066213,0.004375833,0.005260619,0.001510403,0.0002062304,0.00125801,0.00000810959,0.00004320454],"genre_scores_gemma":[0.9676007,0.03087229,0.0006556917,0.0001743627,0.0005431421,0.00002451108,0.00002433644,0.00001257119,0.00009239169],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2158441,"threshold_uncertainty_score":0.4638129,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1120666940728018,"score_gpt":0.2925200491739042,"score_spread":0.1804533551011024,"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."}}