{"id":"W2529466367","doi":"10.2139/ssrn.2845809","title":"Markov-Switching GARCH Models in R: The MSGARCH Package","year":2016,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":46,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval; Université de Sherbrooke; Center for Interuniversity Research and Analysis on Organizations; HEC Montréal","funders":"","keywords":"Autoregressive conditional heteroskedasticity; Econometrics; Markov chain; Computer science; Conditional variance; Volatility (finance); Markov chain Monte Carlo; Bayesian probability; Value at risk; Variable-order Markov model; Markov model; Mathematics; Economics; Risk management; Machine learning; Finance; Artificial intelligence","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":[0.004947837,0.003171663,0.003251376,0.002658633,0.0006163645,0.002738344,0.004086697,0.002314013,0.1153894],"category_scores_gemma":[0.0256796,0.002171237,0.003786689,0.002716841,0.0006351362,0.002948022,0.001709488,0.003903794,0.05667728],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008337624,"about_ca_system_score_gemma":0.002406897,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005892669,"about_ca_topic_score_gemma":0.005756435,"domain_scores_codex":[0.9977432,0.001089106,0.0002146115,0.0003591825,0.0003843098,0.0002096228],"domain_scores_gemma":[0.9876034,0.008590925,0.0008438339,0.001894562,0.0008581504,0.0002092156],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007938084,0.0003310523,0.005849872,0.002762932,0.002156734,0.0005810315,0.0004708008,0.1574593,0.003431113,0.1349956,0.5463592,0.1448084],"study_design_scores_gemma":[0.0009275359,0.000200264,0.00276197,0.0004743226,0.0006414029,0.00056831,0.00008619352,0.5742954,0.007097825,0.2124358,0.2001131,0.0003978205],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004900496,0.001071517,0.7385602,0.0008268779,0.000517579,0.0002573999,0.07835868,0.1708795,0.00462782],"genre_scores_gemma":[0.08037681,0.001526829,0.7738884,0.0006777578,0.0005469014,0.001817703,0.04601575,0.08635093,0.008798959],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1153894,"threshold_uncertainty_score":0.3860159,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02978488619015747,"score_gpt":0.2352682197272764,"score_spread":0.2054833335371189,"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."}}