{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:R5O2EVK5XM5SCUHIVJVDXTDTCU","short_pith_number":"pith:R5O2EVK5","schema_version":"1.0","canonical_sha256":"8f5da2555dbb3b2150e8aa6a3bcc73150ddbcb1e2b3cea1218f866d25eb38e04","source":{"kind":"arxiv","id":"1812.10556","version":1},"attestation_state":"computed","paper":{"title":"Bayesian Approach for Parameter Estimation of Continuous-Time Stochastic Volatility Models using Fourier Transform Methods","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ME","stat.TH"],"primary_cat":"math.ST","authors_text":"Milan Merkle, Rodrigo S. Targino, Yuri F. Saporito","submitted_at":"2018-12-10T20:10:45Z","abstract_excerpt":"We propose a two stage procedure for the estimation of the parameters of a fairly general, continuous-time stochastic volatility. An important ingredient of the proposed method is the Cuchiero-Teichmann volatility estimator, which is based on Fourier transforms and provides a continuous time estimate of the latent process. This estimate is then used to construct an approximate likelihood for the parameters of interest, whose restrictions are taken into account through prior distributions. The procedure is shown to be highly successful for constructing the posterior distribution of the paramete"},"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":"1812.10556","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2018-12-10T20:10:45Z","cross_cats_sorted":["stat.ME","stat.TH"],"title_canon_sha256":"389ea2beec3f00f012623d559a0c09627db458de7e84286d0e4e69b54768a37b","abstract_canon_sha256":"5a84ca752f146fbd6704d16b297c18f28dbda85d707a5af434daa3a070ffcd20"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:57:20.711540Z","signature_b64":"VnR7/aiHySH72drqvTv++iMjFydcvMi4IYAytxf9tFa61fEzsB5Qn4Xwl5V7zX3H+i1FRsuQV/NzKSFWwMKwCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8f5da2555dbb3b2150e8aa6a3bcc73150ddbcb1e2b3cea1218f866d25eb38e04","last_reissued_at":"2026-05-17T23:57:20.710883Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:57:20.710883Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Bayesian Approach for Parameter Estimation of Continuous-Time Stochastic Volatility Models using Fourier Transform Methods","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ME","stat.TH"],"primary_cat":"math.ST","authors_text":"Milan Merkle, Rodrigo S. Targino, Yuri F. Saporito","submitted_at":"2018-12-10T20:10:45Z","abstract_excerpt":"We propose a two stage procedure for the estimation of the parameters of a fairly general, continuous-time stochastic volatility. An important ingredient of the proposed method is the Cuchiero-Teichmann volatility estimator, which is based on Fourier transforms and provides a continuous time estimate of the latent process. This estimate is then used to construct an approximate likelihood for the parameters of interest, whose restrictions are taken into account through prior distributions. The procedure is shown to be highly successful for constructing the posterior distribution of the paramete"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.10556","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":""},"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":"1812.10556","created_at":"2026-05-17T23:57:20.710980+00:00"},{"alias_kind":"arxiv_version","alias_value":"1812.10556v1","created_at":"2026-05-17T23:57:20.710980+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.10556","created_at":"2026-05-17T23:57:20.710980+00:00"},{"alias_kind":"pith_short_12","alias_value":"R5O2EVK5XM5S","created_at":"2026-05-18T12:32:50.500415+00:00"},{"alias_kind":"pith_short_16","alias_value":"R5O2EVK5XM5SCUHI","created_at":"2026-05-18T12:32:50.500415+00:00"},{"alias_kind":"pith_short_8","alias_value":"R5O2EVK5","created_at":"2026-05-18T12:32:50.500415+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/R5O2EVK5XM5SCUHIVJVDXTDTCU","json":"https://pith.science/pith/R5O2EVK5XM5SCUHIVJVDXTDTCU.json","graph_json":"https://pith.science/api/pith-number/R5O2EVK5XM5SCUHIVJVDXTDTCU/graph.json","events_json":"https://pith.science/api/pith-number/R5O2EVK5XM5SCUHIVJVDXTDTCU/events.json","paper":"https://pith.science/paper/R5O2EVK5"},"agent_actions":{"view_html":"https://pith.science/pith/R5O2EVK5XM5SCUHIVJVDXTDTCU","download_json":"https://pith.science/pith/R5O2EVK5XM5SCUHIVJVDXTDTCU.json","view_paper":"https://pith.science/paper/R5O2EVK5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1812.10556&json=true","fetch_graph":"https://pith.science/api/pith-number/R5O2EVK5XM5SCUHIVJVDXTDTCU/graph.json","fetch_events":"https://pith.science/api/pith-number/R5O2EVK5XM5SCUHIVJVDXTDTCU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/R5O2EVK5XM5SCUHIVJVDXTDTCU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/R5O2EVK5XM5SCUHIVJVDXTDTCU/action/storage_attestation","attest_author":"https://pith.science/pith/R5O2EVK5XM5SCUHIVJVDXTDTCU/action/author_attestation","sign_citation":"https://pith.science/pith/R5O2EVK5XM5SCUHIVJVDXTDTCU/action/citation_signature","submit_replication":"https://pith.science/pith/R5O2EVK5XM5SCUHIVJVDXTDTCU/action/replication_record"}},"created_at":"2026-05-17T23:57:20.710980+00:00","updated_at":"2026-05-17T23:57:20.710980+00:00"}