{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:J3I2NZDKLNSEDQSHQZICPSRI6D","short_pith_number":"pith:J3I2NZDK","schema_version":"1.0","canonical_sha256":"4ed1a6e46a5b6441c247865027ca28f0c7470e77efb150470e0c5877248de5cf","source":{"kind":"arxiv","id":"2207.10539","version":1},"attestation_state":"computed","paper":{"title":"Estimating value at risk: LSTM vs. GARCH","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["q-fin.ST","stat.ML"],"primary_cat":"q-fin.RM","authors_text":"Marcin Pitera, Sajad Safarveisi, Thorsten Schmidt, Weronika Ormaniec","submitted_at":"2022-07-21T15:26:07Z","abstract_excerpt":"Estimating value-at-risk on time series data with possibly heteroscedastic dynamics is a highly challenging task. Typically, we face a small data problem in combination with a high degree of non-linearity, causing difficulties for both classical and machine-learning estimation algorithms. In this paper, we propose a novel value-at-risk estimator using a long short-term memory (LSTM) neural network and compare its performance to benchmark GARCH estimators.\n  Our results indicate that even for a relatively short time series, the LSTM could be used to refine or monitor risk estimation processes a"},"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":"2207.10539","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-fin.RM","submitted_at":"2022-07-21T15:26:07Z","cross_cats_sorted":["q-fin.ST","stat.ML"],"title_canon_sha256":"a361c9bb49cac6b837d363c44da7666bf98a5f91fba1bb2024d7d828e52ca76a","abstract_canon_sha256":"e2131081ba6a64030a1e06efec7eddae76ff0e92fe8dc826b00144d80ee7020b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:42:26.439714Z","signature_b64":"Cs4oT8P4tnqPuY/zho5YYpybPWh3C6zDG1Sq0t27rvxaqW1WAZxDHBodAU8XXOQWWKTMBuwQaxjX53ACU3jSDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4ed1a6e46a5b6441c247865027ca28f0c7470e77efb150470e0c5877248de5cf","last_reissued_at":"2026-07-05T04:42:26.439201Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:42:26.439201Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Estimating value at risk: LSTM vs. GARCH","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["q-fin.ST","stat.ML"],"primary_cat":"q-fin.RM","authors_text":"Marcin Pitera, Sajad Safarveisi, Thorsten Schmidt, Weronika Ormaniec","submitted_at":"2022-07-21T15:26:07Z","abstract_excerpt":"Estimating value-at-risk on time series data with possibly heteroscedastic dynamics is a highly challenging task. Typically, we face a small data problem in combination with a high degree of non-linearity, causing difficulties for both classical and machine-learning estimation algorithms. In this paper, we propose a novel value-at-risk estimator using a long short-term memory (LSTM) neural network and compare its performance to benchmark GARCH estimators.\n  Our results indicate that even for a relatively short time series, the LSTM could be used to refine or monitor risk estimation processes a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2207.10539","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/2207.10539/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":"2207.10539","created_at":"2026-07-05T04:42:26.439261+00:00"},{"alias_kind":"arxiv_version","alias_value":"2207.10539v1","created_at":"2026-07-05T04:42:26.439261+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2207.10539","created_at":"2026-07-05T04:42:26.439261+00:00"},{"alias_kind":"pith_short_12","alias_value":"J3I2NZDKLNSE","created_at":"2026-07-05T04:42:26.439261+00:00"},{"alias_kind":"pith_short_16","alias_value":"J3I2NZDKLNSEDQSH","created_at":"2026-07-05T04:42:26.439261+00:00"},{"alias_kind":"pith_short_8","alias_value":"J3I2NZDK","created_at":"2026-07-05T04:42:26.439261+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.01278","citing_title":"Risk forecasting using Long Short-Term Memory Mixture Density Networks","ref_index":60,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/J3I2NZDKLNSEDQSHQZICPSRI6D","json":"https://pith.science/pith/J3I2NZDKLNSEDQSHQZICPSRI6D.json","graph_json":"https://pith.science/api/pith-number/J3I2NZDKLNSEDQSHQZICPSRI6D/graph.json","events_json":"https://pith.science/api/pith-number/J3I2NZDKLNSEDQSHQZICPSRI6D/events.json","paper":"https://pith.science/paper/J3I2NZDK"},"agent_actions":{"view_html":"https://pith.science/pith/J3I2NZDKLNSEDQSHQZICPSRI6D","download_json":"https://pith.science/pith/J3I2NZDKLNSEDQSHQZICPSRI6D.json","view_paper":"https://pith.science/paper/J3I2NZDK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2207.10539&json=true","fetch_graph":"https://pith.science/api/pith-number/J3I2NZDKLNSEDQSHQZICPSRI6D/graph.json","fetch_events":"https://pith.science/api/pith-number/J3I2NZDKLNSEDQSHQZICPSRI6D/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/J3I2NZDKLNSEDQSHQZICPSRI6D/action/timestamp_anchor","attest_storage":"https://pith.science/pith/J3I2NZDKLNSEDQSHQZICPSRI6D/action/storage_attestation","attest_author":"https://pith.science/pith/J3I2NZDKLNSEDQSHQZICPSRI6D/action/author_attestation","sign_citation":"https://pith.science/pith/J3I2NZDKLNSEDQSHQZICPSRI6D/action/citation_signature","submit_replication":"https://pith.science/pith/J3I2NZDKLNSEDQSHQZICPSRI6D/action/replication_record"}},"created_at":"2026-07-05T04:42:26.439261+00:00","updated_at":"2026-07-05T04:42:26.439261+00:00"}