{"id":"W1556455486","doi":"10.1016/j.spa.2010.09.001","title":"The tail empirical process for long memory stochastic volatility sequences","year":2010,"lang":"en","type":"article","venue":"Stochastic Processes and their Applications","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":41,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Agence Nationale de la Recherche","keywords":"Mathematics; Estimator; Volatility (finance); Long memory; Econometrics; Stochastic volatility; Point process; Limiting; Stochastic process; Statistical physics; Heavy-tailed distribution; Empirical research; Hurst exponent; Statistics; Random variable","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004570907,0.00139139,0.00130626,0.002651092,0.0008174201,0.003036228,0.001804518,0.003281459,0.006438103],"category_scores_gemma":[0.03298096,0.0008338486,0.0009605134,0.002363726,0.003416018,0.00740856,0.00214493,0.003728628,0.001070705],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001278161,"about_ca_system_score_gemma":0.0008995476,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002093109,"about_ca_topic_score_gemma":0.001490228,"domain_scores_codex":[0.9993223,0.0002655227,0.00004373232,0.000138386,0.0001493172,0.0000807856],"domain_scores_gemma":[0.9895864,0.006792489,0.001274564,0.000584416,0.001054401,0.0007078168],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00006578804,0.00004192976,0.001499107,0.0001198562,0.00003234082,0.0002600983,0.0002919907,0.04544802,0.002202682,0.9367166,0.001475123,0.01184657],"study_design_scores_gemma":[0.00001623559,0.00002183405,0.0009975752,0.00005761169,0.00002311269,0.0002012536,0.00005976106,0.4412257,0.0004611314,0.555586,0.001305012,0.00004466494],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1061058,0.004500314,0.8767835,0.00258109,0.000334442,0.00006006594,0.0002405453,0.0003945885,0.008999731],"genre_scores_gemma":[0.9159091,0.006782437,0.04115575,0.0008396405,0.001138976,0.0002088271,0.0006448358,0.0003660964,0.03295432],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006438103,"threshold_uncertainty_score":0.0241735,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0417701512573691,"score_gpt":0.289263660245095,"score_spread":0.2474935089877259,"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."}}