{"id":"W2129502867","doi":"","title":"Measuring Causality between Volatility and Returns with High-Frequency Data","year":2008,"lang":"en","type":"article","venue":"RePEc: Research Papers in Economics","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Volatility (finance); Forward volatility; Econometrics; Economics; Volatility smile; Volatility swap; Volatility risk premium; Variance swap; Implied volatility; Realized variance; Futures contract; Stochastic volatility; Autoregressive model; Leverage (statistics); Financial economics; Mathematics; Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001947143,0.0004992028,0.0004582886,0.003644944,0.0003349143,0.001343684,0.0004493493,0.001103138,0.001367579],"category_scores_gemma":[0.0134629,0.0002755908,0.0004535678,0.004172887,0.0003773254,0.001658848,0.0008323542,0.000978605,0.0002699636],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003123087,"about_ca_system_score_gemma":0.0001872069,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002608906,"about_ca_topic_score_gemma":0.002167714,"domain_scores_codex":[0.9986522,0.0003886749,0.0001291285,0.0002220819,0.000459777,0.0001481912],"domain_scores_gemma":[0.9846028,0.009316463,0.003980107,0.001160523,0.0006016727,0.0003384565],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002301328,0.0006189622,0.9152832,0.0001583163,0.0005057147,0.0008090385,0.0003897348,0.02706563,0.009931453,0.004558106,0.0008999395,0.03954981],"study_design_scores_gemma":[0.00002664754,0.0002296394,0.920046,0.00003191058,0.00008293251,0.000485914,0.0003132511,0.06942148,0.003820699,0.004324521,0.001161329,0.00005562252],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9898155,0.0001933192,0.007278221,0.000155033,0.00001448646,0.00002430702,0.001100358,0.00006558671,0.001353176],"genre_scores_gemma":[0.9954501,0.0001105185,0.003269107,0.00002308645,0.00004149027,0.00001221885,0.0009595738,0.000008222923,0.0001257263],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003644944,"threshold_uncertainty_score":0.01029766,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1292505881676278,"score_gpt":0.2881174354904092,"score_spread":0.1588668473227814,"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."}}