{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002610057,0.001852414,0.001550341,0.002035183,0.0004177006,0.001952963,0.002221481,0.001413092,0.0718322],"category_scores_gemma":[0.0119889,0.001509783,0.002512977,0.002619586,0.0003575232,0.002202043,0.001396701,0.002584716,0.03446539],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000555753,"about_ca_system_score_gemma":0.001636551,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006889879,"about_ca_topic_score_gemma":0.004925034,"domain_scores_codex":[0.9986021,0.0004687802,0.0001106474,0.0002362389,0.000446776,0.0001355904],"domain_scores_gemma":[0.9962423,0.002070218,0.0004708067,0.0006044828,0.0005284804,0.00008373262],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001856429,0.0001628712,0.004885367,0.001058811,0.0007663825,0.0004157104,0.0003225672,0.1327076,0.003354709,0.1123256,0.5566924,0.1871223],"study_design_scores_gemma":[0.0002339774,0.00007838884,0.003173611,0.0002840899,0.0002133481,0.0003877924,0.00005577148,0.4994173,0.006257522,0.147375,0.3422526,0.0002705133],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.002030153,0.0003769684,0.8738119,0.0003951622,0.0002079494,0.0001116491,0.02778204,0.08987264,0.005411529],"genre_scores_gemma":[0.05386022,0.00113507,0.8323595,0.0007493639,0.0003592736,0.001607229,0.04296527,0.05506806,0.01189604],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.0718322,"threshold_uncertainty_score":0.2403027,"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."}}