{"id":"W2602104357","doi":"10.2139/ssrn.2918413","title":"Forecasting Performance of Markov-Switching GARCH Models: A Large-Scale Empirical Study","year":2017,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Sherbrooke; HEC Montréal","funders":"","keywords":"Econometrics; Markov chain; Autoregressive conditional heteroskedasticity; Equity (law); Scale (ratio); Economics; Expected shortfall; Value at risk; Mathematics; Statistics; Risk management; Volatility (finance); Finance; Geography","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":[0.00500204,0.0006008544,0.00101532,0.0009584483,0.0004064578,0.001030547,0.001080512,0.001323038,0.000833797],"category_scores_gemma":[0.016054,0.0003414579,0.0008688179,0.001050357,0.0004411215,0.0017908,0.0003711855,0.001158865,0.0001917848],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005198006,"about_ca_system_score_gemma":0.0004087194,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006192397,"about_ca_topic_score_gemma":0.002847989,"domain_scores_codex":[0.9992404,0.000368394,0.00004425162,0.0001318837,0.0001289714,0.00008613669],"domain_scores_gemma":[0.9822863,0.01494173,0.001051857,0.0008745373,0.0005788661,0.0002667717],"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":[0.00130234,0.0007682188,0.1186046,0.0002030777,0.0006470492,0.0004075686,0.0004881706,0.8035402,0.003219583,0.01004486,0.003081713,0.05769254],"study_design_scores_gemma":[0.00001885146,0.00008206418,0.0118999,0.00001001579,0.00004916406,0.0000400248,0.00004356131,0.985023,0.0002937041,0.002383556,0.000138642,0.00001744838],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9917242,0.0006144327,0.006821186,0.0002135505,0.00002618409,0.000006987755,0.00008523071,0.00007546955,0.0004328593],"genre_scores_gemma":[0.9981967,0.0002403556,0.001148078,0.00001418332,0.00002533006,0.000004263663,0.0001968496,0.00001010163,0.0001641963],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006192397,"threshold_uncertainty_score":0.02645361,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07675994195860789,"score_gpt":0.2897608101005286,"score_spread":0.2130008681419207,"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."}}