{"id":"W3124989062","doi":"10.1142/s0219024913500349","title":"VALUING EARLY-EXERCISE INTEREST-RATE OPTIONS WITH MULTI-FACTOR AFFINE MODELS","year":2013,"lang":"en","type":"article","venue":"International Journal of Theoretical and Applied Finance","topic":"Stochastic processes and financial applications","field":"Economics, Econometrics and Finance","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fields Institute for Research in Mathematical Sciences; Actua; Western University; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Accrual; Callable bond; Interest rate derivative; Range (aeronautics); Monte Carlo method; Partial differential equation; Affine transformation; Valuation of options; Monte Carlo methods for option pricing; Interest rate; Asian option; Econometrics; Mathematics; Computer science; Mathematical optimization; Applied mathematics; Economics; Finance; Statistics","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":[0.0001732852,0.0001460267,0.0003086163,0.0001319416,0.00008245069,0.0001728426,0.0003843195,0.0000707697,0.000156205],"category_scores_gemma":[0.00004723801,0.0001219196,0.00006935187,0.0001194562,0.0003144588,0.0002934157,0.0000827332,0.0002266955,0.00009469412],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003860331,"about_ca_system_score_gemma":0.00002383684,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001679022,"about_ca_topic_score_gemma":0.000001307704,"domain_scores_codex":[0.9989625,0.000002867238,0.0005585551,0.0002234109,0.00006973414,0.0001828986],"domain_scores_gemma":[0.9990915,0.00007265447,0.0004062084,0.000121065,0.0002219229,0.00008666694],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00007375095,0.000110478,0.000130401,0.00000552927,0.0000358578,0.000002601391,0.000172264,0.000282951,0.0001490543,0.9929994,0.00002768604,0.006009996],"study_design_scores_gemma":[0.0008912982,0.00008933389,0.01080068,0.00009336738,0.00001055719,0.00002766933,0.00003230633,0.00807449,0.0002409648,0.9788784,0.0006618366,0.0001990951],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2952621,0.0005638369,0.7003064,0.001653638,0.0001817064,0.0001837597,0.00005981445,0.00001268066,0.001776067],"genre_scores_gemma":[0.978393,0.0003304888,0.02080993,0.0001580091,0.000159655,0.00004811849,0.000002504108,0.00001713556,0.00008119839],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6831308,"threshold_uncertainty_score":0.4971738,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02336009173402634,"score_gpt":0.2264585561481464,"score_spread":0.2030984644141201,"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."}}