{"id":"W2114765898","doi":"10.2139/ssrn.1538394","title":"Capturing Option Anomalies with a Variance-Dependent Pricing Kernel","year":2011,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Stochastic processes and financial applications","field":"Economics, Econometrics and Finance","cited_by":106,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Variance (accounting); Econometrics; Kernel (algebra); Stochastic discount factor; Variance components; Economics; Computer science; Mathematics; Financial economics; Statistics; Capital asset pricing model; Combinatorics; Accounting","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.0009924893,0.0005800768,0.0006111655,0.000680969,0.0001936335,0.001314152,0.0009183993,0.001670945,0.0005798806],"category_scores_gemma":[0.008117826,0.0004748742,0.0006505161,0.0006370209,0.0005722538,0.002374606,0.001049478,0.001103884,0.0001419519],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002889432,"about_ca_system_score_gemma":0.0004245306,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001126445,"about_ca_topic_score_gemma":0.0006519918,"domain_scores_codex":[0.9996773,0.0001080154,0.00002032075,0.00006472314,0.00008057105,0.00004907258],"domain_scores_gemma":[0.9972356,0.001635889,0.0004403077,0.0002698345,0.0002687218,0.0001497356],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003619979,0.0001822745,0.0122796,0.00009275302,0.0001198339,0.0006581516,0.000128326,0.7786121,0.04171262,0.1236881,0.0007672976,0.04139688],"study_design_scores_gemma":[0.000003062758,0.000007057061,0.0002067786,7.76811e-7,0.000002989807,0.00003063022,0.000002264681,0.9945341,0.0003842068,0.004791371,0.00003197965,0.000004647338],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3231175,0.0001071325,0.6755149,0.0001291133,0.00005728762,0.00001384336,0.00004062475,0.0003438257,0.0006759204],"genre_scores_gemma":[0.97385,0.00007229669,0.025436,0.00002021831,0.00004407203,0.000006032644,0.00004974165,0.0000497969,0.0004718455],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001670945,"threshold_uncertainty_score":0.005248845,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01938744954257548,"score_gpt":0.192521307293219,"score_spread":0.1731338577506435,"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."}}