{"id":"W4413932982","doi":"10.3390/jrfm18090487","title":"Empirical Calibration of XGBoost Model Hyperparameters Using the Bayesian Optimisation Method: The Case of Bitcoin Volatility","year":2025,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Hyperparameter; Calibration; Bayesian probability; Volatility (finance); Econometrics; Bayesian inference; Computer science; Machine learning; Artificial intelligence; Statistics; Economics; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001820501,0.00007846115,0.0002469881,0.00008143791,0.0001103273,0.00001802205,0.0001030676,0.00004162166,0.000004377474],"category_scores_gemma":[0.001231487,0.00004286506,0.00008046752,0.00019333,0.00009447933,0.00005801453,0.00006034215,0.0001548844,1.289543e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002228296,"about_ca_system_score_gemma":0.00004506968,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007449055,"about_ca_topic_score_gemma":0.00001639424,"domain_scores_codex":[0.9988175,0.0003211712,0.0005674627,0.00008014886,0.0001347952,0.00007891981],"domain_scores_gemma":[0.9980464,0.001212826,0.0004501357,0.0001555467,0.0001123423,0.00002277102],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003720223,0.0002530652,0.003852701,0.0004378133,0.0001179926,0.00004915503,0.003153234,0.01165054,0.0001885782,0.3513379,0.0005026321,0.6280844],"study_design_scores_gemma":[0.0002174789,0.00005238183,0.001834623,0.00006272514,0.0002721228,0.00001827955,0.0004800307,0.5989615,0.000196713,0.3978229,0.00004439961,0.00003689829],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2141532,0.00005697801,0.7853547,0.0001263592,0.00006317956,0.0001356086,0.00001128635,0.000001391688,0.00009725331],"genre_scores_gemma":[0.5010036,0.00004776135,0.498882,0.00004361907,0.0000127212,0.000001103856,8.629866e-8,0.00000228515,0.000006861248],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6280475,"threshold_uncertainty_score":0.1747986,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07773602674330977,"score_gpt":0.3912677072820881,"score_spread":0.3135316805387783,"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."}}