{"id":"W3121717060","doi":"10.1017/asb.2018.6","title":"SPATIAL DEPENDENCE AND AGGREGATION IN WEATHER RISK HEDGING: A LÉVY SUBORDINATED HIERARCHICAL ARCHIMEDEAN COPULAS (LSHAC) APPROACH","year":2018,"lang":"en","type":"article","venue":"Astin Bulletin","topic":"Agricultural risk and resilience","field":"Agricultural and Biological Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba; University of Waterloo","funders":"Nanyang Technological University","keywords":"Copula (linguistics); Downside risk; Econometrics; Risk management; Basis risk; Original research; Computer science; Economics; Financial 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":[0.002695615,0.001089968,0.00114916,0.001048308,0.0004889885,0.001569175,0.001930567,0.001000599,0.002331344],"category_scores_gemma":[0.005186993,0.0007401959,0.001622615,0.001129403,0.001112944,0.001747407,0.001447855,0.00182043,0.0002446896],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001290479,"about_ca_system_score_gemma":0.001158674,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01553268,"about_ca_topic_score_gemma":0.008927815,"domain_scores_codex":[0.9990082,0.0004452402,0.00003650937,0.0001898346,0.0001507634,0.0001694284],"domain_scores_gemma":[0.9970255,0.001381597,0.0005167517,0.0003134024,0.0005055147,0.0002572018],"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":[0.00005085229,0.00006646446,0.004632202,0.0000474189,0.0001753273,0.0003013256,0.000136261,0.9113252,0.001110732,0.06887896,0.001103204,0.01217202],"study_design_scores_gemma":[0.00000202678,0.00001326561,0.0005792472,0.000003711451,0.00001732145,0.00001597246,0.00001735369,0.9915245,0.00006436595,0.007613716,0.0001411823,0.000007258794],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.147773,0.0006136282,0.8462674,0.0005101868,0.00008280633,0.00006863458,0.0001863119,0.0001390432,0.004358958],"genre_scores_gemma":[0.9611267,0.0005970545,0.03293108,0.0001101139,0.0001067824,0.00006656411,0.0001545573,0.00006504473,0.004842051],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01553268,"threshold_uncertainty_score":0.0308845,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008442174009933764,"score_gpt":0.1999505825621145,"score_spread":0.1915084085521807,"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."}}