{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2011:BQZFGLQ6H23NQLRBARTREXCFYC","short_pith_number":"pith:BQZFGLQ6","schema_version":"1.0","canonical_sha256":"0c32532e1e3eb6d82e210467125c45c0bdc0c1de8b755212b81a14457a4e3301","source":{"kind":"arxiv","id":"1111.1113","version":2},"attestation_state":"computed","paper":{"title":"Copula-based Hierarchical Aggregation of Correlated Risks. The behaviour of the diversification benefit in Gaussian and Lognormal Trees","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["q-fin.CP","q-fin.PM","q-fin.ST"],"primary_cat":"q-fin.RM","authors_text":"Jean-Philippe Bruneton","submitted_at":"2011-11-04T12:57:34Z","abstract_excerpt":"The benefits of diversifying risks are difficult to estimate quantitatively because of the uncertainties in the dependence structure between the risks. Also, the modelling of multidimensional dependencies is a non-trivial task. This paper focuses on one such technique for portfolio aggregation, namely the aggregation of risks within trees, where dependencies are set at each step of the aggregation with the help of some copulas. We define rigorously this procedure and then study extensively the Gaussian Tree of quite arbitrary size and shape, where individual risks are normal, and where the Gau"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1111.1113","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-fin.RM","submitted_at":"2011-11-04T12:57:34Z","cross_cats_sorted":["q-fin.CP","q-fin.PM","q-fin.ST"],"title_canon_sha256":"3cd9603b4eea1d580715bcacabe26147fd78ed16eb3061a992991be2cb09cbf2","abstract_canon_sha256":"9ed39b5b12a937b8f766784370ccc0ab59f045a877a4d07e5eef851f57e8d239"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T04:08:34.419555Z","signature_b64":"1dDUlwFjToHGW/54Hpws10ytpmuduksTCZatxhIQlyOkokb9MtVJT+Eo4NC6HwWGABzuQnKR7x+03CGoRqNkBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0c32532e1e3eb6d82e210467125c45c0bdc0c1de8b755212b81a14457a4e3301","last_reissued_at":"2026-05-18T04:08:34.418900Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T04:08:34.418900Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Copula-based Hierarchical Aggregation of Correlated Risks. The behaviour of the diversification benefit in Gaussian and Lognormal Trees","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["q-fin.CP","q-fin.PM","q-fin.ST"],"primary_cat":"q-fin.RM","authors_text":"Jean-Philippe Bruneton","submitted_at":"2011-11-04T12:57:34Z","abstract_excerpt":"The benefits of diversifying risks are difficult to estimate quantitatively because of the uncertainties in the dependence structure between the risks. Also, the modelling of multidimensional dependencies is a non-trivial task. This paper focuses on one such technique for portfolio aggregation, namely the aggregation of risks within trees, where dependencies are set at each step of the aggregation with the help of some copulas. We define rigorously this procedure and then study extensively the Gaussian Tree of quite arbitrary size and shape, where individual risks are normal, and where the Gau"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1111.1113","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"1111.1113","created_at":"2026-05-18T04:08:34.418998+00:00"},{"alias_kind":"arxiv_version","alias_value":"1111.1113v2","created_at":"2026-05-18T04:08:34.418998+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1111.1113","created_at":"2026-05-18T04:08:34.418998+00:00"},{"alias_kind":"pith_short_12","alias_value":"BQZFGLQ6H23N","created_at":"2026-05-18T12:26:24.575870+00:00"},{"alias_kind":"pith_short_16","alias_value":"BQZFGLQ6H23NQLRB","created_at":"2026-05-18T12:26:24.575870+00:00"},{"alias_kind":"pith_short_8","alias_value":"BQZFGLQ6","created_at":"2026-05-18T12:26:24.575870+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/BQZFGLQ6H23NQLRBARTREXCFYC","json":"https://pith.science/pith/BQZFGLQ6H23NQLRBARTREXCFYC.json","graph_json":"https://pith.science/api/pith-number/BQZFGLQ6H23NQLRBARTREXCFYC/graph.json","events_json":"https://pith.science/api/pith-number/BQZFGLQ6H23NQLRBARTREXCFYC/events.json","paper":"https://pith.science/paper/BQZFGLQ6"},"agent_actions":{"view_html":"https://pith.science/pith/BQZFGLQ6H23NQLRBARTREXCFYC","download_json":"https://pith.science/pith/BQZFGLQ6H23NQLRBARTREXCFYC.json","view_paper":"https://pith.science/paper/BQZFGLQ6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1111.1113&json=true","fetch_graph":"https://pith.science/api/pith-number/BQZFGLQ6H23NQLRBARTREXCFYC/graph.json","fetch_events":"https://pith.science/api/pith-number/BQZFGLQ6H23NQLRBARTREXCFYC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BQZFGLQ6H23NQLRBARTREXCFYC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BQZFGLQ6H23NQLRBARTREXCFYC/action/storage_attestation","attest_author":"https://pith.science/pith/BQZFGLQ6H23NQLRBARTREXCFYC/action/author_attestation","sign_citation":"https://pith.science/pith/BQZFGLQ6H23NQLRBARTREXCFYC/action/citation_signature","submit_replication":"https://pith.science/pith/BQZFGLQ6H23NQLRBARTREXCFYC/action/replication_record"}},"created_at":"2026-05-18T04:08:34.418998+00:00","updated_at":"2026-05-18T04:08:34.418998+00:00"}