{"id":"W1990355114","doi":"10.1016/j.irfa.2010.12.001","title":"Modeling investment guarantees in Japan: A risk-neutral GARCH approach","year":2010,"lang":"en","type":"article","venue":"International Review of Financial Analysis","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Autoregressive conditional heteroskedasticity; Economics; Investment (military); Econometrics; Financial economics; Risk neutral; Finance; Business; Volatility (finance); Political science","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001558207,0.000182093,0.0008642742,0.0007385921,0.00005484364,0.00002433825,0.0004968605,0.0001147637,0.0001673834],"category_scores_gemma":[0.001634599,0.0001941395,0.0004902733,0.001542346,0.00005497829,0.0001976017,0.00008060377,0.0004328575,0.00003724875],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000697568,"about_ca_system_score_gemma":0.000075251,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004140728,"about_ca_topic_score_gemma":0.001393115,"domain_scores_codex":[0.9977023,0.00002602457,0.001417854,0.0004861959,0.0001257023,0.00024191],"domain_scores_gemma":[0.998837,0.00003125484,0.0004757265,0.0003611381,0.0002380975,0.00005677206],"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":[0.0000427462,0.0005349343,0.4020697,0.000738389,0.0003746137,0.000002891533,0.0004659546,0.02393185,0.00003082417,0.5620073,0.0001413111,0.00965954],"study_design_scores_gemma":[0.0003115786,0.00002456046,0.07564466,0.0003373516,0.0001317863,0.000001169185,0.00001092952,0.895135,0.00001723967,0.02465798,0.003454236,0.0002735131],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.935099,0.0147321,0.04110834,0.0002909697,0.0003937334,0.0002839787,0.0002291172,0.00001535583,0.007847394],"genre_scores_gemma":[0.9773283,0.01583463,0.006065594,0.0003923199,0.0001329466,0.00005494939,0.00008588981,0.00001179399,0.00009357969],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8712031,"threshold_uncertainty_score":0.791678,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02645241078380115,"score_gpt":0.2637401897392925,"score_spread":0.2372877789554913,"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."}}