{"id":"W1944920263","doi":"10.1002/fut.20540","title":"Time‐varying jump risk premia in stock index futures returns","year":2011,"lang":"en","type":"article","venue":"Journal of Futures Markets","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University","funders":"","keywords":"Autoregressive conditional heteroskedasticity; Economics; Jump; Futures contract; Econometrics; Volatility (finance); Stock index futures; Index (typography); Autoregressive model; Stock market index; Stock (firearms); Futures market; Risk premium; Financial economics; Stock market; Computer science; Physics","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":[0.001948174,0.0002118924,0.0006112127,0.0004615799,0.0001396088,0.00004580869,0.0004247568,0.0002491021,0.000622199],"category_scores_gemma":[0.000533608,0.0002084861,0.0002716324,0.0002744705,0.00004559281,0.0004662342,0.00006955853,0.0008424807,0.00003691024],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001408502,"about_ca_system_score_gemma":0.00006058515,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002144875,"about_ca_topic_score_gemma":0.000118928,"domain_scores_codex":[0.9979112,0.00007499396,0.001276333,0.0002743767,0.0001036911,0.0003594652],"domain_scores_gemma":[0.998144,0.00008987247,0.001257357,0.0002895822,0.00008947278,0.0001296701],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00148278,0.0002962432,0.9628663,0.00006449156,0.000134008,0.00008612753,0.007627352,0.0003694239,0.00004634045,0.001146998,0.008862998,0.01701695],"study_design_scores_gemma":[0.001139144,0.000157714,0.9481691,0.0001165167,0.00001797645,0.00003287215,0.0001999003,0.01608531,0.00006178957,0.02684493,0.006848302,0.0003264963],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9793517,0.006198112,0.0008567306,0.00008232537,0.001323854,0.0001423388,0.00003369827,0.00001384883,0.01199737],"genre_scores_gemma":[0.9952385,0.001299374,0.00231984,0.00009215144,0.0006948637,0.000002684711,0.000001503609,0.00002890431,0.0003221883],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02569794,"threshold_uncertainty_score":0.8501815,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02871888563683917,"score_gpt":0.2173394660243233,"score_spread":0.1886205803874841,"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."}}