{"id":"W4238189779","doi":"10.32920/ryerson.14646741.v1","title":"Pricing spark spread options in electricity markets","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Climate Change Policy and Economics","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"SPARK (programming language); Econometrics; Jump diffusion; Natural gas prices; Electricity; Jump; Monte Carlo method; Electricity market; Electricity price; Valuation of options; Economics; Derivative (finance); Computer science; Financial economics; Engineering; Mathematics; Statistics; Physics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.002265661,0.0005460612,0.0008304461,0.0005974279,0.0005356396,0.00134821,0.0009302417,0.001169415,0.003245967],"category_scores_gemma":[0.007781935,0.0004968269,0.0009473704,0.0005973844,0.001239998,0.00157448,0.001109238,0.001755371,0.0003804095],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008837411,"about_ca_system_score_gemma":0.0008310106,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003414309,"about_ca_topic_score_gemma":0.00267773,"domain_scores_codex":[0.9994164,0.0003231537,0.00001711254,0.00004654912,0.0001578143,0.00003890423],"domain_scores_gemma":[0.997426,0.002066212,0.00009867197,0.00009193748,0.000216031,0.0001011061],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003473266,0.00005507391,0.0008285795,0.00004166858,0.00003282754,0.000111476,0.0001041814,0.7445095,0.001043348,0.2362794,0.000947986,0.01601127],"study_design_scores_gemma":[0.000004098652,0.000005448652,0.00004773955,0.000004146887,0.000001520229,0.000009511058,0.000004293323,0.978639,0.0001008741,0.0208334,0.0003461607,0.000003728632],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04582431,0.0004145448,0.9481324,0.0004994945,0.0000709331,0.0000397345,0.00003369378,0.0001181235,0.004866771],"genre_scores_gemma":[0.6756293,0.001333369,0.3028402,0.0002230792,0.0002583623,0.0001758241,0.0001651017,0.0002814539,0.01909343],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003414309,"threshold_uncertainty_score":0.01198208,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1037019668205352,"score_gpt":0.266241445956119,"score_spread":0.1625394791355838,"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."}}