{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:PI3D55GHL7USEUYFC5PEJPDUVF","short_pith_number":"pith:PI3D55GH","schema_version":"1.0","canonical_sha256":"7a363ef4c75fe9225305175e44bc74a95f4c7c654ea5ca764ef112d55ccc1ca4","source":{"kind":"arxiv","id":"1712.02138","version":2},"attestation_state":"computed","paper":{"title":"A cluster driven log-volatility factor model: a deepening on the source of the volatility clustering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"q-fin.ST","authors_text":"Anshul Verma, Riccardo Junior Buonocore, Tiziana Di Matteo","submitted_at":"2017-12-06T11:40:42Z","abstract_excerpt":"We introduce a new factor model for log volatilities that performs dimensionality reduction and considers contributions globally through the market, and locally through cluster structure and their interactions. We do not assume a-priori the number of clusters in the data, instead using the Directed Bubble Hierarchical Tree (DBHT) algorithm to fix the number of factors. We use the factor model and a new integrated non parametric proxy to study how volatilities contribute to volatility clustering. Globally, only the market contributes to the volatility clustering. Locally for some clusters, the "},"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":"1712.02138","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-fin.ST","submitted_at":"2017-12-06T11:40:42Z","cross_cats_sorted":[],"title_canon_sha256":"c1a0159f86c9d3695332b529505d75bdcff729aa90f0f8da42225474a4974262","abstract_canon_sha256":"027543296651acc6da045c6303bafc3732efe4444ddef7b076366280a1c654cf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-04T23:51:17.380637Z","signature_b64":"1hwLWYLZLgnhUY3G6SFrwMAw2fvugGWa98u6uFddRVZybgCArAWF+oWv53KCJlOMlAOSN+v3jv15TIH6YVWpAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7a363ef4c75fe9225305175e44bc74a95f4c7c654ea5ca764ef112d55ccc1ca4","last_reissued_at":"2026-07-04T23:51:17.380249Z","signature_status":"signed_v1","first_computed_at":"2026-07-04T23:51:17.380249Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A cluster driven log-volatility factor model: a deepening on the source of the volatility clustering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"q-fin.ST","authors_text":"Anshul Verma, Riccardo Junior Buonocore, Tiziana Di Matteo","submitted_at":"2017-12-06T11:40:42Z","abstract_excerpt":"We introduce a new factor model for log volatilities that performs dimensionality reduction and considers contributions globally through the market, and locally through cluster structure and their interactions. We do not assume a-priori the number of clusters in the data, instead using the Directed Bubble Hierarchical Tree (DBHT) algorithm to fix the number of factors. We use the factor model and a new integrated non parametric proxy to study how volatilities contribute to volatility clustering. Globally, only the market contributes to the volatility clustering. Locally for some clusters, the "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1712.02138","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1712.02138/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":"1712.02138","created_at":"2026-07-04T23:51:17.380302+00:00"},{"alias_kind":"arxiv_version","alias_value":"1712.02138v2","created_at":"2026-07-04T23:51:17.380302+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1712.02138","created_at":"2026-07-04T23:51:17.380302+00:00"},{"alias_kind":"pith_short_12","alias_value":"PI3D55GHL7US","created_at":"2026-07-04T23:51:17.380302+00:00"},{"alias_kind":"pith_short_16","alias_value":"PI3D55GHL7USEUYF","created_at":"2026-07-04T23:51:17.380302+00:00"},{"alias_kind":"pith_short_8","alias_value":"PI3D55GH","created_at":"2026-07-04T23:51:17.380302+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2508.05831","citing_title":"Optimal Linear Baseline Models for Scientific Machine Learning","ref_index":86,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PI3D55GHL7USEUYFC5PEJPDUVF","json":"https://pith.science/pith/PI3D55GHL7USEUYFC5PEJPDUVF.json","graph_json":"https://pith.science/api/pith-number/PI3D55GHL7USEUYFC5PEJPDUVF/graph.json","events_json":"https://pith.science/api/pith-number/PI3D55GHL7USEUYFC5PEJPDUVF/events.json","paper":"https://pith.science/paper/PI3D55GH"},"agent_actions":{"view_html":"https://pith.science/pith/PI3D55GHL7USEUYFC5PEJPDUVF","download_json":"https://pith.science/pith/PI3D55GHL7USEUYFC5PEJPDUVF.json","view_paper":"https://pith.science/paper/PI3D55GH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1712.02138&json=true","fetch_graph":"https://pith.science/api/pith-number/PI3D55GHL7USEUYFC5PEJPDUVF/graph.json","fetch_events":"https://pith.science/api/pith-number/PI3D55GHL7USEUYFC5PEJPDUVF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PI3D55GHL7USEUYFC5PEJPDUVF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PI3D55GHL7USEUYFC5PEJPDUVF/action/storage_attestation","attest_author":"https://pith.science/pith/PI3D55GHL7USEUYFC5PEJPDUVF/action/author_attestation","sign_citation":"https://pith.science/pith/PI3D55GHL7USEUYFC5PEJPDUVF/action/citation_signature","submit_replication":"https://pith.science/pith/PI3D55GHL7USEUYFC5PEJPDUVF/action/replication_record"}},"created_at":"2026-07-04T23:51:17.380302+00:00","updated_at":"2026-07-04T23:51:17.380302+00:00"}