{"id":"W2556358723","doi":"10.1016/j.jedc.2018.01.014","title":"Evaluation of counterparty risk for derivatives with early-exercise features","year":2018,"lang":"en","type":"article","venue":"Journal of Economic Dynamics and Control","topic":"Credit Risk and Financial Regulations","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Group for Research in Decision Analysis; HEC Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Credit risk; Counterparty; Valuation (finance); Credit valuation adjustment; Computer science; Risk analysis (engineering); Actuarial science; Economics; Medicine; Finance","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001497438,0.00009842234,0.000427295,0.0001570395,0.0001123456,0.00005798527,0.0001004302,0.00006125301,0.00002703652],"category_scores_gemma":[0.000115328,0.00008902155,0.0001050057,0.00004026293,0.0001557323,0.0002219056,0.000008944003,0.00007692956,0.000002794758],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001255799,"about_ca_system_score_gemma":0.0001123058,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009121357,"about_ca_topic_score_gemma":0.0004189976,"domain_scores_codex":[0.9990898,0.00001495599,0.000589598,0.0001360706,0.00004662477,0.0001229722],"domain_scores_gemma":[0.9981355,0.00008597332,0.001260773,0.0001177278,0.0003478778,0.00005214777],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001597634,0.0001697541,0.6394362,0.00003133155,0.0007975397,6.698469e-7,0.001607082,0.003463343,0.00003220723,0.2823567,0.0005151915,0.06999232],"study_design_scores_gemma":[0.004699028,0.0008641928,0.6585183,0.00004530632,0.0002500682,0.00000859479,0.0001220675,0.2678474,0.00002758695,0.06657533,0.0008800907,0.0001620175],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9552011,0.001244166,0.04163499,0.0001510429,0.0003378023,0.0002731328,0.0004513164,0.000002324818,0.0007041377],"genre_scores_gemma":[0.9986426,0.0002153175,0.0007112319,0.000008109774,0.0003294448,0.00001109045,0.000003528401,0.00001226934,0.00006636474],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.264384,"threshold_uncertainty_score":0.3630193,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01332086373737974,"score_gpt":0.2395284746919048,"score_spread":0.2262076109545251,"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."}}