{"id":"W2795740364","doi":"10.3390/jrfm11020018","title":"Value-at-Risk for South-East Asian Stock Markets: Stochastic Volatility vs. GARCH","year":2018,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Innosuisse - Schweizerische Agentur für Innovationsförderung","keywords":"Econometrics; Value at risk; Volatility (finance); Autoregressive conditional heteroskedasticity; Stochastic volatility; Economics; Stock (firearms); Financial economics; Stock market index; Forward volatility; Stock market; Risk management; Geography; Finance","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002559963,0.0002742391,0.0007073881,0.0004229463,0.0006151542,0.00008656965,0.0003175351,0.000155986,0.00007096206],"category_scores_gemma":[0.0007652951,0.0002843092,0.0003178771,0.000266857,0.0001622692,0.0002471016,0.0001949357,0.0003524101,0.00003746265],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001776803,"about_ca_system_score_gemma":0.0000376363,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007771936,"about_ca_topic_score_gemma":0.00008801801,"domain_scores_codex":[0.9975582,0.00005047685,0.001291309,0.0004755457,0.0001342375,0.0004902454],"domain_scores_gemma":[0.9979447,0.00008628362,0.001253314,0.0003471337,0.0001868475,0.0001817386],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.007359057,0.0006123002,0.4312975,0.0004289237,0.0002935728,0.00002465233,0.01290675,0.0007270158,0.000002949411,0.08344483,0.005732804,0.4571696],"study_design_scores_gemma":[0.00371742,0.001159495,0.678646,0.0001405123,0.0002018074,0.00001126817,0.000381606,0.06533538,0.000004084824,0.1328387,0.1169606,0.0006031909],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4419613,0.001335261,0.5540755,0.0001206175,0.0007709716,0.0004322086,0.0003300579,0.00001356314,0.0009605],"genre_scores_gemma":[0.9890401,0.0004985626,0.009102355,0.00008670293,0.0008803438,0.00001859484,0.000004321373,0.00003211994,0.0003368893],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5470788,"threshold_uncertainty_score":0.9999609,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02033681404444448,"score_gpt":0.2264524991438546,"score_spread":0.2061156850994101,"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."}}