{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:XF4AMBEMYSQNZ2DLE64JYB6EMP","short_pith_number":"pith:XF4AMBEM","schema_version":"1.0","canonical_sha256":"b97806048cc4a0dce86b27b89c07c463f452f84da29fb439b8ac2a462c0d2b8c","source":{"kind":"arxiv","id":"2606.12446","version":1},"attestation_state":"computed","paper":{"title":"Temporal Coarse-Graining of Latent Default-Probability Paths Generates Effective Default Correlation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["physics.data-an"],"primary_cat":"q-fin.ST","authors_text":"Shintaro Mori","submitted_at":"2026-05-30T04:42:41Z","abstract_excerpt":"We show that persistent dynamics of a latent default-probability path can generate effective default correlation through temporal coarse-graining. In the OU--Binomial baseline, monthly defaults are conditionally independent given this latent path, but aggregating monthly default probabilities into long-horizon probabilities induces a scale-dependent effective mixing distribution for aggregated default counts. Applied to corporate default-count data, this mechanism explains long-horizon overdispersion, autocorrelation, and the emergence of effective default correlation. We then examine Davis--L"},"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":"2606.12446","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-fin.ST","submitted_at":"2026-05-30T04:42:41Z","cross_cats_sorted":["physics.data-an"],"title_canon_sha256":"4559986a2dd2bce2a25d34d6384b6f5052eb15105e24c7a1dd8671fa50085d4d","abstract_canon_sha256":"205f2413825a699be061bc214dea3a10c7131616fcf6131fbad77f607d410480"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-12T00:07:50.972570Z","signature_b64":"w+8Izz/ztl+diAvWF8WwG9rbqN9Uj4B0o8+VBwIWNF7JvXGi3jv0OcUeN0W/gnRFfqERNeL6PIZg8UzKQeVmCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b97806048cc4a0dce86b27b89c07c463f452f84da29fb439b8ac2a462c0d2b8c","last_reissued_at":"2026-06-12T00:07:50.971960Z","signature_status":"signed_v1","first_computed_at":"2026-06-12T00:07:50.971960Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Temporal Coarse-Graining of Latent Default-Probability Paths Generates Effective Default Correlation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["physics.data-an"],"primary_cat":"q-fin.ST","authors_text":"Shintaro Mori","submitted_at":"2026-05-30T04:42:41Z","abstract_excerpt":"We show that persistent dynamics of a latent default-probability path can generate effective default correlation through temporal coarse-graining. In the OU--Binomial baseline, monthly defaults are conditionally independent given this latent path, but aggregating monthly default probabilities into long-horizon probabilities induces a scale-dependent effective mixing distribution for aggregated default counts. Applied to corporate default-count data, this mechanism explains long-horizon overdispersion, autocorrelation, and the emergence of effective default correlation. We then examine Davis--L"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.12446","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2606.12446/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2606.12446","created_at":"2026-06-12T00:07:50.972051+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.12446v1","created_at":"2026-06-12T00:07:50.972051+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.12446","created_at":"2026-06-12T00:07:50.972051+00:00"},{"alias_kind":"pith_short_12","alias_value":"XF4AMBEMYSQN","created_at":"2026-06-12T00:07:50.972051+00:00"},{"alias_kind":"pith_short_16","alias_value":"XF4AMBEMYSQNZ2DL","created_at":"2026-06-12T00:07:50.972051+00:00"},{"alias_kind":"pith_short_8","alias_value":"XF4AMBEM","created_at":"2026-06-12T00:07:50.972051+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/XF4AMBEMYSQNZ2DLE64JYB6EMP","json":"https://pith.science/pith/XF4AMBEMYSQNZ2DLE64JYB6EMP.json","graph_json":"https://pith.science/api/pith-number/XF4AMBEMYSQNZ2DLE64JYB6EMP/graph.json","events_json":"https://pith.science/api/pith-number/XF4AMBEMYSQNZ2DLE64JYB6EMP/events.json","paper":"https://pith.science/paper/XF4AMBEM"},"agent_actions":{"view_html":"https://pith.science/pith/XF4AMBEMYSQNZ2DLE64JYB6EMP","download_json":"https://pith.science/pith/XF4AMBEMYSQNZ2DLE64JYB6EMP.json","view_paper":"https://pith.science/paper/XF4AMBEM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.12446&json=true","fetch_graph":"https://pith.science/api/pith-number/XF4AMBEMYSQNZ2DLE64JYB6EMP/graph.json","fetch_events":"https://pith.science/api/pith-number/XF4AMBEMYSQNZ2DLE64JYB6EMP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XF4AMBEMYSQNZ2DLE64JYB6EMP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XF4AMBEMYSQNZ2DLE64JYB6EMP/action/storage_attestation","attest_author":"https://pith.science/pith/XF4AMBEMYSQNZ2DLE64JYB6EMP/action/author_attestation","sign_citation":"https://pith.science/pith/XF4AMBEMYSQNZ2DLE64JYB6EMP/action/citation_signature","submit_replication":"https://pith.science/pith/XF4AMBEMYSQNZ2DLE64JYB6EMP/action/replication_record"}},"created_at":"2026-06-12T00:07:50.972051+00:00","updated_at":"2026-06-12T00:07:50.972051+00:00"}