{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:PD6BSC2A2FVFCWAYWVMW6UESMP","short_pith_number":"pith:PD6BSC2A","schema_version":"1.0","canonical_sha256":"78fc190b40d16a515818b5596f509263f85ad5be3d1509d288b6d350660f5a0d","source":{"kind":"arxiv","id":"2308.01486","version":1},"attestation_state":"computed","paper":{"title":"Path Shadowing Monte-Carlo","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["q-fin.CP","q-fin.PR","q-fin.ST"],"primary_cat":"q-fin.MF","authors_text":"Jean-Philippe Bouchaud, Rudy Morel, St\\'ephane Mallat","submitted_at":"2023-08-03T00:51:38Z","abstract_excerpt":"We introduce a Path Shadowing Monte-Carlo method, which provides prediction of future paths, given any generative model. At any given date, it averages future quantities over generated price paths whose past history matches, or `shadows', the actual (observed) history. We test our approach using paths generated from a maximum entropy model of financial prices, based on a recently proposed multi-scale analogue of the standard skewness and kurtosis called `Scattering Spectra'. This model promotes diversity of generated paths while reproducing the main statistical properties of financial prices, "},"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":"2308.01486","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"q-fin.MF","submitted_at":"2023-08-03T00:51:38Z","cross_cats_sorted":["q-fin.CP","q-fin.PR","q-fin.ST"],"title_canon_sha256":"fc09ffe6cdf565935a3a3c981fc80ef67c746ab5a2cdc4379e950f44e9c1358b","abstract_canon_sha256":"a3326a8eb774a031f7e92339105c1087098fb1679f4c30ab01f29f68b60d7b38"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:37:19.179428Z","signature_b64":"pvv0A2RlMTJC+7b8l4uLZVBZln6nXdxVd3QgWvFRGAFy3UlxL/Ysg9q6yqprzoh2xI2NfnFaKESGovH37+asDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"78fc190b40d16a515818b5596f509263f85ad5be3d1509d288b6d350660f5a0d","last_reissued_at":"2026-07-05T06:37:19.178934Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:37:19.178934Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Path Shadowing Monte-Carlo","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["q-fin.CP","q-fin.PR","q-fin.ST"],"primary_cat":"q-fin.MF","authors_text":"Jean-Philippe Bouchaud, Rudy Morel, St\\'ephane Mallat","submitted_at":"2023-08-03T00:51:38Z","abstract_excerpt":"We introduce a Path Shadowing Monte-Carlo method, which provides prediction of future paths, given any generative model. At any given date, it averages future quantities over generated price paths whose past history matches, or `shadows', the actual (observed) history. We test our approach using paths generated from a maximum entropy model of financial prices, based on a recently proposed multi-scale analogue of the standard skewness and kurtosis called `Scattering Spectra'. This model promotes diversity of generated paths while reproducing the main statistical properties of financial prices, "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.01486","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/2308.01486/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":"2308.01486","created_at":"2026-07-05T06:37:19.178996+00:00"},{"alias_kind":"arxiv_version","alias_value":"2308.01486v1","created_at":"2026-07-05T06:37:19.178996+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.01486","created_at":"2026-07-05T06:37:19.178996+00:00"},{"alias_kind":"pith_short_12","alias_value":"PD6BSC2A2FVF","created_at":"2026-07-05T06:37:19.178996+00:00"},{"alias_kind":"pith_short_16","alias_value":"PD6BSC2A2FVFCWAY","created_at":"2026-07-05T06:37:19.178996+00:00"},{"alias_kind":"pith_short_8","alias_value":"PD6BSC2A","created_at":"2026-07-05T06:37:19.178996+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.03993","citing_title":"Synthetic Data for Portfolios: A Throw of the Dice Will Never Abolish Chance","ref_index":80,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PD6BSC2A2FVFCWAYWVMW6UESMP","json":"https://pith.science/pith/PD6BSC2A2FVFCWAYWVMW6UESMP.json","graph_json":"https://pith.science/api/pith-number/PD6BSC2A2FVFCWAYWVMW6UESMP/graph.json","events_json":"https://pith.science/api/pith-number/PD6BSC2A2FVFCWAYWVMW6UESMP/events.json","paper":"https://pith.science/paper/PD6BSC2A"},"agent_actions":{"view_html":"https://pith.science/pith/PD6BSC2A2FVFCWAYWVMW6UESMP","download_json":"https://pith.science/pith/PD6BSC2A2FVFCWAYWVMW6UESMP.json","view_paper":"https://pith.science/paper/PD6BSC2A","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2308.01486&json=true","fetch_graph":"https://pith.science/api/pith-number/PD6BSC2A2FVFCWAYWVMW6UESMP/graph.json","fetch_events":"https://pith.science/api/pith-number/PD6BSC2A2FVFCWAYWVMW6UESMP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PD6BSC2A2FVFCWAYWVMW6UESMP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PD6BSC2A2FVFCWAYWVMW6UESMP/action/storage_attestation","attest_author":"https://pith.science/pith/PD6BSC2A2FVFCWAYWVMW6UESMP/action/author_attestation","sign_citation":"https://pith.science/pith/PD6BSC2A2FVFCWAYWVMW6UESMP/action/citation_signature","submit_replication":"https://pith.science/pith/PD6BSC2A2FVFCWAYWVMW6UESMP/action/replication_record"}},"created_at":"2026-07-05T06:37:19.178996+00:00","updated_at":"2026-07-05T06:37:19.178996+00:00"}