{"id":"W2944268219","doi":"10.48550/arxiv.1905.03273","title":"Dependencies and systemic risk in the European insurance sector: Some new evidence based on copula-DCC-GARCH model and selected clustering methods","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Systemic risk; Autoregressive conditional heteroskedasticity; Copula (linguistics); Econometrics; Actuarial science; Economics; Cluster analysis; Business; Financial economics; Financial crisis; Statistics; Volatility (finance); Mathematics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00263269,0.0003259575,0.0005852648,0.0003954873,0.0001640864,0.0001293552,0.0005889476,0.0002274017,0.000003848626],"category_scores_gemma":[0.0004133219,0.00035256,0.00009331429,0.0003731324,0.00007266547,0.0003447591,0.0004504443,0.0009406037,0.00001880792],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002000962,"about_ca_system_score_gemma":0.000124209,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002205653,"about_ca_topic_score_gemma":0.0003369983,"domain_scores_codex":[0.9975648,0.000500976,0.0004540305,0.001105907,0.00004172828,0.0003325648],"domain_scores_gemma":[0.9981779,0.0004803781,0.0004470819,0.0007534427,0.0000536891,0.00008745532],"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":[0.00008656939,0.00002291906,0.1952544,0.0002483293,0.00001692088,0.00001335961,0.0007447095,0.7982856,0.00001448112,0.004633777,0.00001135064,0.0006675961],"study_design_scores_gemma":[0.0005002422,0.00004692049,0.1015412,0.0004679023,0.00001994534,0.000002148318,0.00006338281,0.8826004,0.000003487847,0.01441114,0.00001300033,0.0003301805],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7296506,0.002692644,0.2665277,0.00003475663,0.0001433529,0.0003981495,0.00007874266,0.0000349771,0.0004389973],"genre_scores_gemma":[0.9937056,0.004315309,0.001607179,0.0000833437,0.00006065575,0.000001440456,0.000007007306,0.00003423169,0.0001852078],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2649206,"threshold_uncertainty_score":0.9998927,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1737163699900648,"score_gpt":0.2285342580004628,"score_spread":0.054817888010398,"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."}}