{"id":"W4406298674","doi":"10.1111/jtsa.12812","title":"High‐Frequency Instruments and Identification‐Robust Inference for Stochastic Volatility Models","year":2025,"lang":"en","type":"article","venue":"Journal of Time Series Analysis","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Center for Interuniversity Research and Analysis on Organizations; Canada Mortgage and Housing Corporation","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; Mitacs; Concordia University; McGill University; Bank of Canada; Toulouse School of Economics; International Association for Applied Econometrics; Banco Santander; Alexander von Humboldt-Stiftung","keywords":"Stochastic volatility; Volatility (finance); Mathematics; Econometrics; Inference; Confidence interval; Statistics; Computer science; Artificial intelligence","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":[0.0007842996,0.0001165107,0.0006101616,0.0005934003,0.0001592171,0.000112731,0.0001907797,0.00007769563,0.00007104157],"category_scores_gemma":[0.0004073385,0.0001238763,0.0002580738,0.0006431647,0.00005616321,0.0007644068,0.0000416637,0.0001153951,0.00000455746],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000842957,"about_ca_system_score_gemma":0.00005171856,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000226613,"about_ca_topic_score_gemma":0.00005544384,"domain_scores_codex":[0.9984471,0.00001332095,0.001117511,0.0002212644,0.00005011238,0.0001507565],"domain_scores_gemma":[0.9986462,0.00007497645,0.0007436199,0.0002225802,0.000258471,0.00005416866],"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":[0.000445193,0.0003100757,0.2034276,0.0002301029,0.004493503,0.000001540323,0.001062772,0.4512456,0.0002196242,0.3296039,0.0003290951,0.008630931],"study_design_scores_gemma":[0.0003145096,0.00004897658,0.02727154,0.00002086173,0.0003612521,6.359981e-7,0.0000427731,0.6551255,0.00002140796,0.3166187,0.00005689487,0.0001170027],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4256441,0.0006177368,0.573009,0.0003290872,0.000104322,0.00007742133,0.00009765929,0.000004771652,0.0001158998],"genre_scores_gemma":[0.9889538,0.0001731303,0.01003505,0.0000305116,0.00003441785,0.000005359965,0.00001191493,0.000005866702,0.0007499742],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5633097,"threshold_uncertainty_score":0.5051528,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02485008492393826,"score_gpt":0.2419082064639717,"score_spread":0.2170581215400334,"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."}}