{"id":"W2593065375","doi":"10.1080/14697688.2019.1588469","title":"Estimation of risk contributions with MCMC","year":2019,"lang":"en","type":"article","venue":"Quantitative Finance","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Japan Society for the Promotion of Science London","keywords":"Estimator; Markov chain Monte Carlo; Value at risk; Monte Carlo method; Econometrics; Mean squared error; Consistency (knowledge bases); Mathematics; Asymptotic distribution; Statistics; Computer science; Risk management; Economics; Finance","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":[0.004240854,0.001099139,0.001416538,0.002369974,0.0008679413,0.001361025,0.002167934,0.001649991,0.004764185],"category_scores_gemma":[0.0242236,0.001173161,0.001133395,0.001839165,0.001177152,0.001635829,0.001573174,0.002577397,0.0007701552],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001561246,"about_ca_system_score_gemma":0.002983374,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.016754,"about_ca_topic_score_gemma":0.0179311,"domain_scores_codex":[0.9985743,0.0006082787,0.00008817614,0.0002205549,0.0003881567,0.0001205351],"domain_scores_gemma":[0.9872577,0.009649153,0.0008285395,0.00100939,0.000993791,0.0002614669],"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":[0.000101118,0.00005223746,0.00337499,0.0000857181,0.0001005141,0.00009907533,0.00006406777,0.923275,0.0007748476,0.03112486,0.001264059,0.03968355],"study_design_scores_gemma":[0.000007373324,0.000004326793,0.0001961608,0.000009374187,0.000005357571,0.00001031811,0.000003488746,0.9918909,0.0002178748,0.007393233,0.0002550076,0.000006605775],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01890455,0.0002725712,0.9784652,0.0001821713,0.00004643373,0.00008708899,0.0001944806,0.0006208181,0.001226762],"genre_scores_gemma":[0.4547922,0.0005058276,0.5388158,0.0002968708,0.0001576555,0.0004754923,0.001262911,0.0003060075,0.00338717],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.016754,"threshold_uncertainty_score":0.03331298,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03523785401312769,"score_gpt":0.3697633568256244,"score_spread":0.3345255028124967,"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."}}