{"id":"W2088894581","doi":"10.1002/asmb.936","title":"Pricing of mountain range derivatives under a principal component stochastic volatility model","year":2012,"lang":"en","type":"article","venue":"Applied Stochastic Models in Business and Industry","topic":"Stochastic processes and financial applications","field":"Economics, Econometrics and Finance","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Stylized fact; Stochastic volatility; Econometrics; Volatility (finance); Principal component analysis; Volatility smile; Economics; Implied volatility; Range (aeronautics); SABR volatility model; Stochastic modelling; Stochastic process; Mathematics; Computer science; Financial economics; Applied mathematics; Mathematical economics; Statistics; Finance; Engineering; Keynesian economics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004515269,0.000284034,0.0006307688,0.000250017,0.0001321906,0.00002550434,0.0002143614,0.0003625963,0.00001951483],"category_scores_gemma":[0.00008728765,0.0003149716,0.00004189157,0.0005989968,0.0002066502,0.0003209383,0.0001696959,0.0004403344,0.00000572072],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001100417,"about_ca_system_score_gemma":0.00006469082,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002853085,"about_ca_topic_score_gemma":0.00001015966,"domain_scores_codex":[0.9980929,0.000004249969,0.000835343,0.0004600041,0.00009613914,0.0005113076],"domain_scores_gemma":[0.9989266,0.0001330516,0.0004058406,0.0003291035,0.0000642665,0.0001411155],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004588462,0.000225448,0.0005885822,0.00007104097,0.00001514843,9.712943e-8,0.000852249,0.2236123,0.00004221127,0.774164,0.000002107462,0.0003808544],"study_design_scores_gemma":[0.0008348479,0.00001081865,0.021073,0.00005677946,0.00001323219,0.000002952227,0.0002559069,0.6094437,0.000005814123,0.3679892,0.0000055902,0.0003082353],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.276133,0.000585916,0.7215145,0.00008698798,0.00005669557,0.0004083433,0.00006411981,0.00002091242,0.001129509],"genre_scores_gemma":[0.9959146,0.00001296174,0.003540681,0.00009470924,0.00009114048,0.000280743,0.00001511323,0.00003434561,0.0000156995],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7197816,"threshold_uncertainty_score":0.9999303,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06010589701416902,"score_gpt":0.2472550043830177,"score_spread":0.1871491073688487,"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."}}