{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:AKKLYPEULAZE775LLTFEO6PBMQ","short_pith_number":"pith:AKKLYPEU","schema_version":"1.0","canonical_sha256":"0294bc3c9458324fffab5cca4779e1643a81dc932d8aa0159fce3d52ef9da66f","source":{"kind":"arxiv","id":"1706.03938","version":1},"attestation_state":"computed","paper":{"title":"Efficient Bayesian inference for multivariate factor stochastic volatility models with leverage","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Chris Carter, David Gunawan, Robert Kohn","submitted_at":"2017-06-13T07:46:22Z","abstract_excerpt":"This paper discusses the efficient Bayesian estimation of a multivariate factor stochastic volatility (Factor MSV) model with leverage. We propose a novel approach to construct the sampling schemes that converges to the posterior distribution of the latent volatilities and the parameters of interest of the Factor MSV model based on recent advances in Particle Markov chain Monte Carlo (PMCMC). As opposed to the approach of Chib et al. (2006} and Omori et al. (2007}, our approach does not require approximating the joint distribution of outcome and volatility innovations by a mixture of bivariate"},"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":"1706.03938","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2017-06-13T07:46:22Z","cross_cats_sorted":[],"title_canon_sha256":"ea7e103315e900f0a47707a55d5f11394609179ecb2e0fb1c3b6c6c3f965717f","abstract_canon_sha256":"a418cf3e5544a20b800e62f3b14e594f2e928413133b021fcf2807a6ee33340d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:42:27.964918Z","signature_b64":"hys4VNwvhNUdGCLau14S0qPGgADcm9F9KeGfXgMfG7iY+Vco4mcdnCRQRU8jaPC6xWDnnXgRJqh9xWkWlqKeCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0294bc3c9458324fffab5cca4779e1643a81dc932d8aa0159fce3d52ef9da66f","last_reissued_at":"2026-05-18T00:42:27.964397Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:42:27.964397Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Efficient Bayesian inference for multivariate factor stochastic volatility models with leverage","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Chris Carter, David Gunawan, Robert Kohn","submitted_at":"2017-06-13T07:46:22Z","abstract_excerpt":"This paper discusses the efficient Bayesian estimation of a multivariate factor stochastic volatility (Factor MSV) model with leverage. We propose a novel approach to construct the sampling schemes that converges to the posterior distribution of the latent volatilities and the parameters of interest of the Factor MSV model based on recent advances in Particle Markov chain Monte Carlo (PMCMC). As opposed to the approach of Chib et al. (2006} and Omori et al. (2007}, our approach does not require approximating the joint distribution of outcome and volatility innovations by a mixture of bivariate"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.03938","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":"1706.03938","created_at":"2026-05-18T00:42:27.964482+00:00"},{"alias_kind":"arxiv_version","alias_value":"1706.03938v1","created_at":"2026-05-18T00:42:27.964482+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.03938","created_at":"2026-05-18T00:42:27.964482+00:00"},{"alias_kind":"pith_short_12","alias_value":"AKKLYPEULAZE","created_at":"2026-05-18T12:31:05.417338+00:00"},{"alias_kind":"pith_short_16","alias_value":"AKKLYPEULAZE775L","created_at":"2026-05-18T12:31:05.417338+00:00"},{"alias_kind":"pith_short_8","alias_value":"AKKLYPEU","created_at":"2026-05-18T12:31:05.417338+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2409.06551","citing_title":"Robust financial calibration: a Bayesian approach for neural SDEs","ref_index":25,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/AKKLYPEULAZE775LLTFEO6PBMQ","json":"https://pith.science/pith/AKKLYPEULAZE775LLTFEO6PBMQ.json","graph_json":"https://pith.science/api/pith-number/AKKLYPEULAZE775LLTFEO6PBMQ/graph.json","events_json":"https://pith.science/api/pith-number/AKKLYPEULAZE775LLTFEO6PBMQ/events.json","paper":"https://pith.science/paper/AKKLYPEU"},"agent_actions":{"view_html":"https://pith.science/pith/AKKLYPEULAZE775LLTFEO6PBMQ","download_json":"https://pith.science/pith/AKKLYPEULAZE775LLTFEO6PBMQ.json","view_paper":"https://pith.science/paper/AKKLYPEU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1706.03938&json=true","fetch_graph":"https://pith.science/api/pith-number/AKKLYPEULAZE775LLTFEO6PBMQ/graph.json","fetch_events":"https://pith.science/api/pith-number/AKKLYPEULAZE775LLTFEO6PBMQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AKKLYPEULAZE775LLTFEO6PBMQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AKKLYPEULAZE775LLTFEO6PBMQ/action/storage_attestation","attest_author":"https://pith.science/pith/AKKLYPEULAZE775LLTFEO6PBMQ/action/author_attestation","sign_citation":"https://pith.science/pith/AKKLYPEULAZE775LLTFEO6PBMQ/action/citation_signature","submit_replication":"https://pith.science/pith/AKKLYPEULAZE775LLTFEO6PBMQ/action/replication_record"}},"created_at":"2026-05-18T00:42:27.964482+00:00","updated_at":"2026-05-18T00:42:27.964482+00:00"}