{"id":"W2767164886","doi":"10.5539/ijsp.v6n6p137","title":"A Family of Non Linear Models in a Market with Semi Markov Regimes: Application to the Commodity and the Derivative Market","year":2017,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Stochastic processes and financial applications","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Army Research Office; Division of Mathematical Sciences","keywords":"Commodity; Markov chain; Heston model; Asset (computer security); Derivative (finance); Econometrics; Mathematics; Applied mathematics; Mathematical economics; Markov process; Mathematical optimization; Economics; Computer science; Financial economics; Stochastic volatility; Statistics; SABR volatility model; Finance","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001404594,0.0005271509,0.0007266609,0.0005927334,0.0005568319,0.001182369,0.0008113199,0.001104778,0.003289512],"category_scores_gemma":[0.004446593,0.0003886581,0.001372862,0.0005058417,0.001206743,0.00173723,0.001071141,0.001664388,0.0002919434],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007059338,"about_ca_system_score_gemma":0.0006839546,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001920901,"about_ca_topic_score_gemma":0.001260291,"domain_scores_codex":[0.9995566,0.0001993621,0.00002269458,0.00008001303,0.0001012396,0.00004014071],"domain_scores_gemma":[0.9983601,0.001070433,0.0002316742,0.0001064258,0.000125865,0.0001053962],"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":[0.00002663802,0.00004986296,0.0007690635,0.00006860193,0.00004247855,0.0002502569,0.0001634762,0.21183,0.001692221,0.7739657,0.000933432,0.01020826],"study_design_scores_gemma":[0.000005803381,0.00001974285,0.0001505178,0.000009334326,0.000007644743,0.00008144484,0.000014509,0.8517783,0.0001485016,0.1468492,0.0009235868,0.00001138868],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03683305,0.0006167054,0.9555718,0.0005802123,0.00005244736,0.00002843112,0.00007176393,0.0001046753,0.006140894],"genre_scores_gemma":[0.8594303,0.001655823,0.1280533,0.0002664779,0.0001842745,0.0001681136,0.0001721113,0.00008759413,0.009981983],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003289512,"threshold_uncertainty_score":0.01100457,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02056097297288979,"score_gpt":0.2554517204560121,"score_spread":0.2348907474831223,"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."}}