{"id":"W2044767136","doi":"10.1142/s0217595910002697","title":"ESTIMATING BIVARIATE GARCH-JUMP MODEL BASED ON HIGH FREQUENCY DATA: THE CASE OF REVALUATION OF THE CHINESE YUAN IN JULY 2005","year":2010,"lang":"en","type":"article","venue":"Asia Pacific Journal of Operational Research","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Kwansei Gakuin University","keywords":"Jump; Bivariate analysis; Autoregressive conditional heteroskedasticity; Volatility (finance); Liberian dollar; Econometrics; Us dollar; Exchange rate; Univariate; Economics; Mathematics; Poisson distribution; Statistics; Multivariate statistics; Monetary economics; Finance; Physics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01268745,0.00009744881,0.0002785872,0.0003738585,0.0002440086,0.000061639,0.0006504571,0.00007604071,0.00008798826],"category_scores_gemma":[0.006071751,0.00006429596,0.00007935102,0.0005144781,0.0001655058,0.000394564,0.00009434466,0.0009966993,0.000007448924],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007566182,"about_ca_system_score_gemma":0.0005953129,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007400709,"about_ca_topic_score_gemma":0.0005104851,"domain_scores_codex":[0.9979587,0.0001804703,0.001105567,0.0002151512,0.0003208053,0.0002192808],"domain_scores_gemma":[0.997644,0.0006503919,0.0004707837,0.0006183809,0.0005655813,0.00005086295],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002293899,0.0004845585,0.06714024,0.00007158409,0.00003862569,0.0000280118,0.00150686,0.6333801,0.00462217,0.2899494,0.0004883046,0.002060726],"study_design_scores_gemma":[0.0003746446,0.00006206241,0.02875162,0.00006034466,0.0000030214,0.00002336156,0.00006093246,0.9069729,0.00006253657,0.06355002,0.00002357837,0.00005500074],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9827696,0.0002031405,0.01111626,0.002998896,0.0002817909,0.0002779265,0.0002470759,0.000001327664,0.002103989],"genre_scores_gemma":[0.9870096,0.0000248605,0.0127091,0.00001470634,0.0001548597,0.000005806909,0.00001127642,0.00001142548,0.00005835564],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2735927,"threshold_uncertainty_score":0.7268892,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1193717849016678,"score_gpt":0.37424887662704,"score_spread":0.2548770917253722,"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."}}