{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:FQMGDCOESYAC4BXOOVRP5MFRX5","short_pith_number":"pith:FQMGDCOE","schema_version":"1.0","canonical_sha256":"2c186189c496002e06ee7562feb0b1bf7fcdddc9c8aba88b514766be08eb11b3","source":{"kind":"arxiv","id":"2308.01419","version":1},"attestation_state":"computed","paper":{"title":"Graph Neural Networks for Forecasting Multivariate Realized Volatility with Spillover Effects","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.LG","q-fin.RM"],"primary_cat":"q-fin.ST","authors_text":"Chao Zhang, Mihai Cucuringu, Xiaowen Dong, Xingyue Pu","submitted_at":"2023-08-01T14:39:03Z","abstract_excerpt":"We present a novel methodology for modeling and forecasting multivariate realized volatilities using customized graph neural networks to incorporate spillover effects across stocks. The proposed model offers the benefits of incorporating spillover effects from multi-hop neighbors, capturing nonlinear relationships, and flexible training with different loss functions. Our empirical findings provide compelling evidence that incorporating spillover effects from multi-hop neighbors alone does not yield a clear advantage in terms of predictive accuracy. However, modeling nonlinear spillover effects"},"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":"2308.01419","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"q-fin.ST","submitted_at":"2023-08-01T14:39:03Z","cross_cats_sorted":["cs.LG","q-fin.RM"],"title_canon_sha256":"adb472a81a16cd8b57af0dbab4544a7d47750a73fc97eabb6957601f665c40c7","abstract_canon_sha256":"50acfaeb194e24f41d4b78acebaac2288ea218f8d5e0926c715adb25178f9745"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:37:18.124887Z","signature_b64":"vL0847loVB2i1ip72j0quLu2R00Igh6Lr8rQZuD8m+rhT4WETdOwJ7OMsW2LBGMWj0gazuRGJVUXdIo/ABFWDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2c186189c496002e06ee7562feb0b1bf7fcdddc9c8aba88b514766be08eb11b3","last_reissued_at":"2026-07-05T06:37:18.124357Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:37:18.124357Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Graph Neural Networks for Forecasting Multivariate Realized Volatility with Spillover Effects","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.LG","q-fin.RM"],"primary_cat":"q-fin.ST","authors_text":"Chao Zhang, Mihai Cucuringu, Xiaowen Dong, Xingyue Pu","submitted_at":"2023-08-01T14:39:03Z","abstract_excerpt":"We present a novel methodology for modeling and forecasting multivariate realized volatilities using customized graph neural networks to incorporate spillover effects across stocks. The proposed model offers the benefits of incorporating spillover effects from multi-hop neighbors, capturing nonlinear relationships, and flexible training with different loss functions. Our empirical findings provide compelling evidence that incorporating spillover effects from multi-hop neighbors alone does not yield a clear advantage in terms of predictive accuracy. However, modeling nonlinear spillover effects"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.01419","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/2308.01419/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":"2308.01419","created_at":"2026-07-05T06:37:18.124425+00:00"},{"alias_kind":"arxiv_version","alias_value":"2308.01419v1","created_at":"2026-07-05T06:37:18.124425+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.01419","created_at":"2026-07-05T06:37:18.124425+00:00"},{"alias_kind":"pith_short_12","alias_value":"FQMGDCOESYAC","created_at":"2026-07-05T06:37:18.124425+00:00"},{"alias_kind":"pith_short_16","alias_value":"FQMGDCOESYAC4BXO","created_at":"2026-07-05T06:37:18.124425+00:00"},{"alias_kind":"pith_short_8","alias_value":"FQMGDCOE","created_at":"2026-07-05T06:37:18.124425+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/FQMGDCOESYAC4BXOOVRP5MFRX5","json":"https://pith.science/pith/FQMGDCOESYAC4BXOOVRP5MFRX5.json","graph_json":"https://pith.science/api/pith-number/FQMGDCOESYAC4BXOOVRP5MFRX5/graph.json","events_json":"https://pith.science/api/pith-number/FQMGDCOESYAC4BXOOVRP5MFRX5/events.json","paper":"https://pith.science/paper/FQMGDCOE"},"agent_actions":{"view_html":"https://pith.science/pith/FQMGDCOESYAC4BXOOVRP5MFRX5","download_json":"https://pith.science/pith/FQMGDCOESYAC4BXOOVRP5MFRX5.json","view_paper":"https://pith.science/paper/FQMGDCOE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2308.01419&json=true","fetch_graph":"https://pith.science/api/pith-number/FQMGDCOESYAC4BXOOVRP5MFRX5/graph.json","fetch_events":"https://pith.science/api/pith-number/FQMGDCOESYAC4BXOOVRP5MFRX5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FQMGDCOESYAC4BXOOVRP5MFRX5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FQMGDCOESYAC4BXOOVRP5MFRX5/action/storage_attestation","attest_author":"https://pith.science/pith/FQMGDCOESYAC4BXOOVRP5MFRX5/action/author_attestation","sign_citation":"https://pith.science/pith/FQMGDCOESYAC4BXOOVRP5MFRX5/action/citation_signature","submit_replication":"https://pith.science/pith/FQMGDCOESYAC4BXOOVRP5MFRX5/action/replication_record"}},"created_at":"2026-07-05T06:37:18.124425+00:00","updated_at":"2026-07-05T06:37:18.124425+00:00"}