{"id":"W3123059542","doi":"10.18637/jss.v091.i04","title":"Markov-Switching GARCH Models in <i>R</i>: The <b>MSGARCH</b> Package","year":2019,"lang":"en","type":"article","venue":"Journal of Statistical Software","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":107,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institut de Valorisation des Données; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Autoregressive conditional heteroskedasticity; Heteroscedasticity; Econometrics; Markov chain; Markov chain Monte Carlo; Conditional variance; Computer science; Autoregressive model; Volatility (finance); Bayesian probability; Mathematics; Machine learning; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001906551,0.0001275355,0.0004525925,0.0001659659,0.00006465697,0.00008123033,0.0003865582,0.00008376558,0.0002237892],"category_scores_gemma":[0.0007089787,0.0001012696,0.0001168946,0.0001941908,0.00003229371,0.0003248675,0.00004916598,0.0006432895,0.0001146286],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007820853,"about_ca_system_score_gemma":0.0000480118,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001508284,"about_ca_topic_score_gemma":0.00002197322,"domain_scores_codex":[0.9983038,0.00005283193,0.0009888345,0.0001977073,0.0001402983,0.0003164723],"domain_scores_gemma":[0.9985716,0.0007089794,0.0003497631,0.0002275019,0.00006447197,0.00007764843],"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":[0.00029313,0.0003544482,0.3300195,0.0001888947,0.0000448353,0.0001552266,0.004357736,0.003451394,0.00002410714,0.6185455,0.001904747,0.04066047],"study_design_scores_gemma":[0.001042887,0.0002682068,0.111917,0.0001049998,0.000009415534,0.00004202159,0.0002825916,0.05106796,0.000003602917,0.8309583,0.004043642,0.0002594014],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3042232,0.0008535141,0.6926445,0.0003524289,0.0002452935,0.0001326038,0.0001069767,0.000006580698,0.001434922],"genre_scores_gemma":[0.9718545,0.0001350163,0.02748656,0.0003413704,0.000103,0.000002324603,0.000003919534,0.00001717167,0.00005611104],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6676313,"threshold_uncertainty_score":0.4129655,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03152622886809661,"score_gpt":0.2488539276623972,"score_spread":0.2173276987943006,"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."}}