{"id":"W4281850289","doi":"10.3390/jrfm15060247","title":"The Use of Principal Component Analysis (PCA) in Building Yield Curve Scenarios and Identifying Relative-Value Trading Opportunities on the Romanian Government Bond Market","year":2022,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Yield curve; Principal component analysis; Yield (engineering); Explanatory power; Econometrics; Economics; Bond; Financial economics; Statistics; Mathematics; 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":[0.002183487,0.0007836687,0.0004592464,0.00329756,0.0004294248,0.001644099,0.0003255435,0.0004308854,0.001465986],"category_scores_gemma":[0.007813175,0.0002376892,0.0008773927,0.002456867,0.0004718128,0.001008118,0.000754517,0.0009613787,0.0003649794],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003429627,"about_ca_system_score_gemma":0.0005524186,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005786039,"about_ca_topic_score_gemma":0.003293941,"domain_scores_codex":[0.9992631,0.0003300754,0.00005736358,0.0001447306,0.0001126194,0.00009209716],"domain_scores_gemma":[0.9980279,0.001039794,0.00035394,0.0002407343,0.000248417,0.00008916604],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004928316,0.0002718782,0.5592943,0.000188762,0.0005739349,0.00127229,0.001423813,0.2062495,0.009979735,0.01645313,0.004831261,0.1989685],"study_design_scores_gemma":[0.00002110751,0.00009195429,0.430053,0.00007250621,0.0000583612,0.0002799318,0.0006285607,0.5562608,0.001649392,0.007089003,0.003709667,0.00008569677],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9639528,0.0003661256,0.03119441,0.0002598141,0.00003212521,0.00005626505,0.0007776156,0.0002187835,0.003142092],"genre_scores_gemma":[0.989714,0.0001720021,0.00877541,0.00001289974,0.00002310553,0.00002872529,0.0009438072,0.00002930442,0.000300747],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005786039,"threshold_uncertainty_score":0.01154751,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08418157595986447,"score_gpt":0.2343819338216885,"score_spread":0.150200357861824,"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."}}