{"id":"W3126073774","doi":"10.2139/ssrn.2968981","title":"How to Predict Financial Stress? An Assessment of Markov Switching Models","year":2017,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance and Financial Risk Management","field":"Economics, Econometrics and Finance","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"Bank of Canada","funders":"","keywords":"Markov chain; Stress (linguistics); Markov model; Stress testing (software); Econometrics; Business; Actuarial science; Economics; Computer science; Machine learning; Programming language","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":[0.006348468,0.0009840735,0.001836868,0.001065315,0.0005049942,0.001729358,0.001561961,0.001960921,0.00228935],"category_scores_gemma":[0.01638875,0.0005905953,0.001255721,0.000669031,0.0007547541,0.001947218,0.0009201944,0.001716467,0.0002214704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009638223,"about_ca_system_score_gemma":0.0009984081,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0128892,"about_ca_topic_score_gemma":0.007185701,"domain_scores_codex":[0.9991058,0.0005042692,0.00004472357,0.0001492935,0.00006816895,0.0001277527],"domain_scores_gemma":[0.9808328,0.01690699,0.0008496583,0.00034322,0.0006044547,0.0004627694],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002653635,0.0001439315,0.01859794,0.0000712357,0.0001860812,0.0001016818,0.0001588845,0.9340961,0.0002426324,0.02955252,0.0009407687,0.01564284],"study_design_scores_gemma":[0.000007938708,0.000022125,0.000804219,0.00000884463,0.00001669059,0.000008474609,0.00001246006,0.9904078,0.00002780132,0.008623574,0.00005244967,0.000007625944],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6703867,0.001950479,0.3176342,0.004405014,0.0002447152,0.0001629166,0.0007284442,0.0003419674,0.004145562],"genre_scores_gemma":[0.987656,0.0007641039,0.00923478,0.0001666947,0.0001672078,0.00008145445,0.0003394877,0.00001934814,0.001570838],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0128892,"threshold_uncertainty_score":0.03357428,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01921622741958436,"score_gpt":0.2592401359418224,"score_spread":0.2400239085222381,"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."}}