{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:KZFRRTIWRSMONFDQCVFJOXZ33P","short_pith_number":"pith:KZFRRTIW","schema_version":"1.0","canonical_sha256":"564b18cd168c98e69470154a975f3bdbffd5c2802cb3c6b13fd3439da8fd1897","source":{"kind":"arxiv","id":"2402.11930","version":2},"attestation_state":"computed","paper":{"title":"Stylized Facts of High-Frequency Bitcoin Time Series","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.AP"],"primary_cat":"q-fin.ST","authors_text":"Fernando Alonso-Marroquin, Karina Arias-Calluari, Michael S. Harr\\'e, M. N. Najafi, Yaoyue Tang","submitted_at":"2024-02-19T08:17:24Z","abstract_excerpt":"This paper analyses the high-frequency intraday Bitcoin dataset from 2019 to 2022. During this time frame, the Bitcoin market index exhibited two distinct periods, 2019-20 and 2021-22, characterized by an abrupt change in volatility. The Bitcoin price returns for both periods can be described by an anomalous diffusion process, transitioning from subdiffusion for short intervals to weak superdiffusion over longer time intervals. The characteristic features related to this anomalous behavior studied in the present paper include heavy tails, which can be described using a $q$-Gaussian distributio"},"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":"2402.11930","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"q-fin.ST","submitted_at":"2024-02-19T08:17:24Z","cross_cats_sorted":["stat.AP"],"title_canon_sha256":"b60eb998034dc730e041aa7d313d70dfc12d5ce368ced5e3e3af86455f9cdd53","abstract_canon_sha256":"b8db05ebd7e02c1ad2ce22547d3c9d1c358a9938a1bdd4ab1dfa561b91b0d607"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:25:00.666810Z","signature_b64":"Q11JOEdPfV8qEgtlHn0Vy28FklsaZ5F6WW5jQIeQXtXoUqFD3iNPmYKQgO1XKepzZXjvR2zG9s4yVooa/H67BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"564b18cd168c98e69470154a975f3bdbffd5c2802cb3c6b13fd3439da8fd1897","last_reissued_at":"2026-07-05T11:25:00.666337Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:25:00.666337Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Stylized Facts of High-Frequency Bitcoin Time Series","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.AP"],"primary_cat":"q-fin.ST","authors_text":"Fernando Alonso-Marroquin, Karina Arias-Calluari, Michael S. Harr\\'e, M. N. Najafi, Yaoyue Tang","submitted_at":"2024-02-19T08:17:24Z","abstract_excerpt":"This paper analyses the high-frequency intraday Bitcoin dataset from 2019 to 2022. During this time frame, the Bitcoin market index exhibited two distinct periods, 2019-20 and 2021-22, characterized by an abrupt change in volatility. The Bitcoin price returns for both periods can be described by an anomalous diffusion process, transitioning from subdiffusion for short intervals to weak superdiffusion over longer time intervals. The characteristic features related to this anomalous behavior studied in the present paper include heavy tails, which can be described using a $q$-Gaussian distributio"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.11930","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/2402.11930/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":"2402.11930","created_at":"2026-07-05T11:25:00.666398+00:00"},{"alias_kind":"arxiv_version","alias_value":"2402.11930v2","created_at":"2026-07-05T11:25:00.666398+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.11930","created_at":"2026-07-05T11:25:00.666398+00:00"},{"alias_kind":"pith_short_12","alias_value":"KZFRRTIWRSMO","created_at":"2026-07-05T11:25:00.666398+00:00"},{"alias_kind":"pith_short_16","alias_value":"KZFRRTIWRSMONFDQ","created_at":"2026-07-05T11:25:00.666398+00:00"},{"alias_kind":"pith_short_8","alias_value":"KZFRRTIW","created_at":"2026-07-05T11:25:00.666398+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.04880","citing_title":"A Harmonic Mean Formulation of Average Reward Reinforcement Learning in SMDPs","ref_index":23,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KZFRRTIWRSMONFDQCVFJOXZ33P","json":"https://pith.science/pith/KZFRRTIWRSMONFDQCVFJOXZ33P.json","graph_json":"https://pith.science/api/pith-number/KZFRRTIWRSMONFDQCVFJOXZ33P/graph.json","events_json":"https://pith.science/api/pith-number/KZFRRTIWRSMONFDQCVFJOXZ33P/events.json","paper":"https://pith.science/paper/KZFRRTIW"},"agent_actions":{"view_html":"https://pith.science/pith/KZFRRTIWRSMONFDQCVFJOXZ33P","download_json":"https://pith.science/pith/KZFRRTIWRSMONFDQCVFJOXZ33P.json","view_paper":"https://pith.science/paper/KZFRRTIW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2402.11930&json=true","fetch_graph":"https://pith.science/api/pith-number/KZFRRTIWRSMONFDQCVFJOXZ33P/graph.json","fetch_events":"https://pith.science/api/pith-number/KZFRRTIWRSMONFDQCVFJOXZ33P/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KZFRRTIWRSMONFDQCVFJOXZ33P/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KZFRRTIWRSMONFDQCVFJOXZ33P/action/storage_attestation","attest_author":"https://pith.science/pith/KZFRRTIWRSMONFDQCVFJOXZ33P/action/author_attestation","sign_citation":"https://pith.science/pith/KZFRRTIWRSMONFDQCVFJOXZ33P/action/citation_signature","submit_replication":"https://pith.science/pith/KZFRRTIWRSMONFDQCVFJOXZ33P/action/replication_record"}},"created_at":"2026-07-05T11:25:00.666398+00:00","updated_at":"2026-07-05T11:25:00.666398+00:00"}