{"id":"W4399622673","doi":"10.3390/jrfm17060248","title":"Adaptive Conformal Inference for Computing Market Risk Measures: An Analysis with Four Thousand Crypto-Assets","year":2024,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Inference; Computer science; Econometrics; Conformal map; Actuarial science; Mathematics; Risk analysis (engineering); Economics; Business; Artificial intelligence","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":[0.002414565,0.0002263906,0.000664988,0.0007284285,0.0003069359,0.0002522595,0.0002041098,0.00009943359,0.00001501467],"category_scores_gemma":[0.0001866143,0.0001999642,0.0002645314,0.0005453064,0.00005835325,0.0005406543,0.00006174568,0.0003532532,0.000003457021],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007729811,"about_ca_system_score_gemma":0.00005008791,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002226953,"about_ca_topic_score_gemma":0.0002727688,"domain_scores_codex":[0.9982389,0.00004073026,0.0009018125,0.0003700336,0.0001215778,0.0003269476],"domain_scores_gemma":[0.9986566,0.0001952399,0.0006832217,0.000190227,0.0001481713,0.0001264834],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00129214,0.0001607017,0.3758608,0.000186246,0.0009703254,0.0000778349,0.003105909,0.01498203,4.129344e-7,0.108197,0.0003555889,0.4948111],"study_design_scores_gemma":[0.001465424,0.001068623,0.4755197,0.0001851515,0.001034043,0.000009961052,0.0005252442,0.4341474,0.000002519423,0.04026379,0.04530196,0.0004761553],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3117473,0.002209053,0.6848387,0.00001997816,0.0002344899,0.0001968554,0.0001702636,0.00001620757,0.0005671635],"genre_scores_gemma":[0.9734816,0.004740486,0.02139704,0.00003012185,0.0002628683,0.000006810868,0.000004138834,0.00001993724,0.00005701571],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6634416,"threshold_uncertainty_score":0.8154305,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02538443891450715,"score_gpt":0.2405050066585673,"score_spread":0.2151205677440601,"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."}}