{"id":"W2922247736","doi":"10.5430/ijfr.v10n2p61","title":"Risk Analysis of the Stock Price Index of Countries Participating in the “Belt and Road” Initiative - Based on GARCH-VaR Model","year":2019,"lang":"en","type":"article","venue":"International Journal of Financial Research","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"China; Stock market; Stock (firearms); Autoregressive conditional heteroskedasticity; Stock market index; Index (typography); Foreign direct investment; Business; Economics; International economics; Finance; Macroeconomics; Volatility (finance); Political science; Geography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.001634444,0.000721969,0.0006362725,0.001212871,0.0002860716,0.001218549,0.0007305803,0.0005356577,0.001625571],"category_scores_gemma":[0.00350141,0.0002319738,0.001140644,0.0009589142,0.000287022,0.001240131,0.0006632869,0.0007941028,0.0002448922],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005620656,"about_ca_system_score_gemma":0.0006184864,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01626275,"about_ca_topic_score_gemma":0.007638301,"domain_scores_codex":[0.9993388,0.0001362459,0.00005478217,0.0001752484,0.0001788548,0.0001160248],"domain_scores_gemma":[0.998565,0.0005377027,0.0003809505,0.0001064718,0.0003072483,0.0001026272],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005316092,0.0002003585,0.5983976,0.00015821,0.0009407756,0.001828473,0.0002635707,0.3457399,0.003213574,0.007533069,0.004129339,0.03706344],"study_design_scores_gemma":[0.00002745974,0.0001850287,0.1203828,0.00002024333,0.000207353,0.0002091288,0.0001851217,0.8749538,0.001520341,0.001670486,0.0005889668,0.0000492991],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.97655,0.0006176731,0.01924906,0.0003364915,0.00004337515,0.00004788105,0.0009053198,0.0001721829,0.002077824],"genre_scores_gemma":[0.9963121,0.0002022271,0.001333893,0.00002230641,0.00002129317,0.00001837537,0.001163039,0.000008398204,0.0009184817],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01626275,"threshold_uncertainty_score":0.03233618,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0780639450475369,"score_gpt":0.3530759572976897,"score_spread":0.2750120122501528,"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."}}