{"id":"W3176151014","doi":"10.3390/jrfm14070294","title":"Forecasting Volatility and Tail Risk in Electricity Markets","year":2021,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Volatility (finance); Econometrics; Estimator; Expected shortfall; Autoregressive conditional heteroskedasticity; Economics; Jump; Electricity; Value at risk; Realized variance; Electricity market; Electricity price; Statistics; Risk management; Mathematics; Engineering; Finance","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":[0.0005527835,0.00009953995,0.0001932806,0.0001204976,0.00006865391,0.00003361184,0.00004444402,0.00004524676,0.000006849056],"category_scores_gemma":[0.0001761172,0.0000947649,0.00004160175,0.0002253532,0.00001385907,0.0001156819,0.00004496548,0.000296631,1.819652e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003154317,"about_ca_system_score_gemma":0.00001003829,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001804319,"about_ca_topic_score_gemma":0.00012161,"domain_scores_codex":[0.9992617,0.00004543383,0.0003160139,0.00009623662,0.0001068179,0.0001737697],"domain_scores_gemma":[0.9996637,0.00007966817,0.0001053161,0.00006090008,0.00003527344,0.00005520283],"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":[0.00004507897,0.00002534388,0.1503068,0.000100467,0.00002166086,0.0002631506,0.0003489167,0.00178362,0.00001434317,0.0002278113,0.0001624621,0.8467003],"study_design_scores_gemma":[0.001406813,0.000078856,0.8466586,0.0002794132,0.0001057094,0.0001068415,0.0001732244,0.1097403,0.0002697321,0.004268191,0.03664998,0.0002623895],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9822541,0.00433617,0.01134506,0.000007080146,0.0002323513,0.00003434106,0.000004452235,0.000009727532,0.001776657],"genre_scores_gemma":[0.9881069,0.009057156,0.002684444,0.0000111367,0.0001052415,9.252581e-7,5.426252e-7,0.00000827318,0.00002537083],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8464379,"threshold_uncertainty_score":0.38644,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006678979708117126,"score_gpt":0.1820502572333505,"score_spread":0.1753712775252334,"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."}}