{"id":"W2372684697","doi":"","title":"Index Futures Optimal Dynamic Hedging Ratio Research——Based on DCC-MVGARCH Model","year":2011,"lang":"en","type":"article","venue":"International Business Research","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Futures contract; Stock index futures; Index (typography); Hedge; Econometrics; Economics; Stock market index; Stock (firearms); Mathematics; Financial economics; Stock market; Computer science","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":["metaresearch","insufficient_payload"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.03706567,0.0002832163,0.0003573854,0.005158332,0.0009677719,0.0009170056,0.004441899,0.0002307294,0.002493807],"category_scores_gemma":[0.04968336,0.0002261063,0.0001524482,0.004781841,0.001156085,0.0006522393,0.001280544,0.001729787,0.0007351205],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006023432,"about_ca_system_score_gemma":0.001149132,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003751877,"about_ca_topic_score_gemma":0.0001234062,"domain_scores_codex":[0.9817301,0.002687612,0.0009068598,0.001349344,0.01216162,0.001164442],"domain_scores_gemma":[0.9778463,0.009292263,0.0001804752,0.00138715,0.01099972,0.0002940509],"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":[0.009068809,0.002045025,0.03887609,0.00007115524,0.0001756875,0.0005430173,0.004737601,0.4510663,0.00743415,0.02481299,0.04116597,0.4200033],"study_design_scores_gemma":[0.000587829,0.00009268388,0.1256527,0.00009140284,0.000001744006,0.00001191714,0.0004123412,0.8193038,0.0006141337,0.05137769,0.001642182,0.0002115407],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3030958,0.0000639807,0.5142132,0.007540485,0.001870982,0.0009671542,0.00006504609,0.0001560484,0.1720273],"genre_scores_gemma":[0.9545965,0.00001025387,0.03549875,0.000137578,0.000354768,0.0001752017,0.00001879927,0.00005897542,0.009149136],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6515007,"threshold_uncertainty_score":0.998418,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4831040211871152,"score_gpt":0.5566000300112172,"score_spread":0.07349600882410195,"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."}}