{"id":"W3135002281","doi":"10.2139/ssrn.3737708","title":"Supplementary Material of 'On the Estimation of Jump-Diffusion Models Using High-Frequency Data: A Filtering-Based Approach'","year":2020,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Stochastic processes and financial applications","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"HEC Montréal; Wilfrid Laurier University; Simon Fraser University","funders":"","keywords":"Jump; Jump diffusion; Estimation; Diffusion; Computer science; Econometrics; Mathematics; Engineering; Physics","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001770628,0.001650435,0.001539662,0.00249038,0.0004705446,0.001328132,0.002901876,0.002529235,0.5536885],"category_scores_gemma":[0.0314916,0.0009050498,0.00104321,0.003276296,0.000310279,0.001841703,0.001421525,0.001357738,0.1491171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007295801,"about_ca_system_score_gemma":0.001798372,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005535939,"about_ca_topic_score_gemma":0.007732629,"domain_scores_codex":[0.9991587,0.0001873547,0.0001103353,0.0001463552,0.0003202693,0.00007700353],"domain_scores_gemma":[0.9812537,0.01282893,0.0005958711,0.001763872,0.003177913,0.0003797363],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000136865,0.0002376937,0.0005591759,0.0009428578,0.00008069578,0.0002365284,0.00003080325,0.005612236,0.0009106441,0.01015873,0.9273656,0.05372822],"study_design_scores_gemma":[0.0007905289,0.0003653316,0.008612679,0.0006047577,0.000095805,0.001106283,0.00007437779,0.0730066,0.003939017,0.09046001,0.8207371,0.0002075913],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.004687664,0.002133116,0.2865822,0.009401181,0.025923,0.0007053619,0.6042166,0.01224115,0.05410967],"genre_scores_gemma":[0.04639591,0.003749361,0.2110939,0.005721068,0.009368036,0.001366146,0.6332912,0.005901549,0.08311283],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.5536885,"threshold_uncertainty_score":0.6366092,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05741791207169181,"score_gpt":0.2384631222859859,"score_spread":0.1810452102142941,"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."}}