{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:L7AG3YTLRZ55SPG5M4RXSN7XHM","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"47fefad196c10266002d925e18f3b92104df5a3adc6b6578f770101af6bb90ab","cross_cats_sorted":["cs.LG","econ.EM","math.DS","q-fin.ST"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2018-07-23T00:02:36Z","title_canon_sha256":"d1f9eba9947fda328d9498dee4cf96f91e28e727986c85e66052356e2685d334"},"schema_version":"1.0","source":{"id":"1807.08390","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1807.08390","created_at":"2026-05-18T00:10:08Z"},{"alias_kind":"arxiv_version","alias_value":"1807.08390v1","created_at":"2026-05-18T00:10:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.08390","created_at":"2026-05-18T00:10:08Z"},{"alias_kind":"pith_short_12","alias_value":"L7AG3YTLRZ55","created_at":"2026-05-18T12:32:33Z"},{"alias_kind":"pith_short_16","alias_value":"L7AG3YTLRZ55SPG5","created_at":"2026-05-18T12:32:33Z"},{"alias_kind":"pith_short_8","alias_value":"L7AG3YTL","created_at":"2026-05-18T12:32:33Z"}],"graph_snapshots":[{"event_id":"sha256:e63fada54f146a3293b27f88642623b714db0b40cc067b56720877c37abc8bd2","target":"graph","created_at":"2026-05-18T00:10:08Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"A standard model of (conditional) heteroscedasticity, i.e., the phenomenon that the variance of a process changes over time, is the Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) model, which is especially important for economics and finance. GARCH models are typically estimated by the Quasi-Maximum Likelihood (QML) method, which works under mild statistical assumptions. Here, we suggest a finite sample approach, called ScoPe, to construct distribution-free confidence regions around the QML estimate, which have exact coverage probabilities, despite no additional assumptions ","authors_text":"Bal\\'azs Csan\\'ad Cs\\'aji","cross_cats":["cs.LG","econ.EM","math.DS","q-fin.ST"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2018-07-23T00:02:36Z","title":"Score Permutation Based Finite Sample Inference for Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.08390","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:a9ed690f399fad35cbc32acda6c96777aabb3de53c2a25d04db4d642d0c7e96f","target":"record","created_at":"2026-05-18T00:10:08Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"47fefad196c10266002d925e18f3b92104df5a3adc6b6578f770101af6bb90ab","cross_cats_sorted":["cs.LG","econ.EM","math.DS","q-fin.ST"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2018-07-23T00:02:36Z","title_canon_sha256":"d1f9eba9947fda328d9498dee4cf96f91e28e727986c85e66052356e2685d334"},"schema_version":"1.0","source":{"id":"1807.08390","kind":"arxiv","version":1}},"canonical_sha256":"5fc06de26b8e7bd93cdd67237937f73b17c80f4b0f9f85138d0f72d8fec0231b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5fc06de26b8e7bd93cdd67237937f73b17c80f4b0f9f85138d0f72d8fec0231b","first_computed_at":"2026-05-18T00:10:08.187278Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:10:08.187278Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"8Toh+oEiPzopCsMw6lo5YCeVr4IRYXhBgWNOq9x0lXUiqAsQ2iFjoV1cLractuzYfeN8s8XlZNzvxGhzYN/pDQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:10:08.187913Z","signed_message":"canonical_sha256_bytes"},"source_id":"1807.08390","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a9ed690f399fad35cbc32acda6c96777aabb3de53c2a25d04db4d642d0c7e96f","sha256:e63fada54f146a3293b27f88642623b714db0b40cc067b56720877c37abc8bd2"],"state_sha256":"6afb85af8b27b9da53f08ec3585caa55d859c582857d81d337c608dd02af3bf6"}