{"id":"https://openalex.org/W7106652959","doi":"https://doi.org/10.48550/arxiv.2511.17892","title":"Arbitrage-Free Bond and Yield Curve Forecasting with Neural Filters under HJM Constraints","display_name":"Arbitrage-Free Bond and Yield Curve Forecasting with Neural Filters under HJM Constraints","publication_year":2025,"publication_date":"2025-11-22","ids":{"openalex":"https://openalex.org/W7106652959","doi":"https://doi.org/10.48550/arxiv.2511.17892"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2511.17892","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2511.17892","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2511.17892","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Gao, Xiang","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Gao, Xiang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Hyndman, Cody","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hyndman, Cody","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.7193999886512756,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.7193999886512756,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10047","display_name":"Financial Markets and Investment Strategies","score":0.07079999893903732,"subfield":{"id":"https://openalex.org/subfields/2003","display_name":"Finance"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11653","display_name":"Financial Distress and Bankruptcy Prediction","score":0.022600000724196434,"subfield":{"id":"https://openalex.org/subfields/1402","display_name":"Accounting"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/heath\u2013jarrow\u2013morton-framework","display_name":"Heath\u2013Jarrow\u2013Morton framework","score":0.7731000185012817},{"id":"https://openalex.org/keywords/bond","display_name":"Bond","score":0.6141999959945679},{"id":"https://openalex.org/keywords/yield-curve","display_name":"Yield curve","score":0.5957000255584717},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.578000009059906},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.5562999844551086},{"id":"https://openalex.org/keywords/arbitrage","display_name":"Arbitrage","score":0.45579999685287476},{"id":"https://openalex.org/keywords/yield","display_name":"Yield (engineering)","score":0.41429999470710754},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.36800000071525574}],"concepts":[{"id":"https://openalex.org/C187614132","wikidata":"https://www.wikidata.org/wiki/Q1563747","display_name":"Heath\u2013Jarrow\u2013Morton framework","level":3,"score":0.7731000185012817},{"id":"https://openalex.org/C69738904","wikidata":"https://www.wikidata.org/wiki/Q11693","display_name":"Bond","level":2,"score":0.6141999959945679},{"id":"https://openalex.org/C176230804","wikidata":"https://www.wikidata.org/wiki/Q205257","display_name":"Yield curve","level":3,"score":0.5957000255584717},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.578000009059906},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.5562999844551086},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.46459999680519104},{"id":"https://openalex.org/C160623529","wikidata":"https://www.wikidata.org/wiki/Q273088","display_name":"Arbitrage","level":2,"score":0.45579999685287476},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4311000108718872},{"id":"https://openalex.org/C134121241","wikidata":"https://www.wikidata.org/wiki/Q899301","display_name":"Yield (engineering)","level":2,"score":0.41429999470710754},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3725999891757965},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.36800000071525574},{"id":"https://openalex.org/C32959826","wikidata":"https://www.wikidata.org/wiki/Q2361268","display_name":"Bond valuation","level":3,"score":0.36230000853538513},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.34389999508857727},{"id":"https://openalex.org/C2780889827","wikidata":"https://www.wikidata.org/wiki/Q10756188","display_name":"Treasury","level":2,"score":0.3303000032901764},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.32690000534057617},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.3255000114440918},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.32359999418258667},{"id":"https://openalex.org/C184389593","wikidata":"https://www.wikidata.org/wiki/Q603159","display_name":"Curve fitting","level":2,"score":0.30630001425743103},{"id":"https://openalex.org/C93246554","wikidata":"https://www.wikidata.org/wiki/Q4162534","display_name":"Short-rate model","level":3,"score":0.30320000648498535},{"id":"https://openalex.org/C128263813","wikidata":"https://www.wikidata.org/wiki/Q6588953","display_name":"Forward rate","level":3,"score":0.29249998927116394},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.287200003862381},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.2705000042915344},{"id":"https://openalex.org/C175706884","wikidata":"https://www.wikidata.org/wiki/Q1130194","display_name":"Moving average","level":2,"score":0.25699999928474426},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2542000114917755},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.2522999942302704},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.25220000743865967}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2511.17892","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2511.17892","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2511.17892","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2511.17892","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[{"display_name":"Partnerships for the goals","score":0.4735229015350342,"id":"https://metadata.un.org/sdg/17"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"We":[0],"develop":[1],"an":[2,56],"arbitrage-free":[3],"deep":[4],"learning":[5],"framework":[6],"for":[7,81],"yield":[8],"curve":[9],"and":[10,21,46,54,72,76,84,89,115],"bond":[11,74],"price":[12],"forecasting":[13],"based":[14],"on":[15],"the":[16],"Heath-Jarrow-Morton":[17],"(HJM)":[18],"term-structure":[19],"model":[20,66],"a":[22,32,37],"dynamic":[23],"Nelson-Siegel":[24],"parameterization":[25],"of":[26],"forward":[27],"rates.":[28],"Our":[29],"approach":[30],"embeds":[31],"no-arbitrage":[33],"drift":[34],"restriction":[35],"into":[36],"neural":[38,51],"state-space":[39],"architecture":[40],"by":[41,111],"combining":[42],"Kalman,":[43,45],"extended":[44],"particle":[47],"filters":[48],"with":[49],"recurrent":[50],"networks":[52],"(LSTM/CLSTM),":[53],"introduces":[55],"explicit":[57],"arbitrage":[58,93],"error":[59],"regularization":[60,94],"(AER)":[61],"term":[62],"during":[63],"training.":[64],"The":[65],"is":[67,79],"applied":[68],"to":[69,96],"U.S.":[70],"Treasury":[71],"corporate":[73],"data,":[75],"its":[77,97],"performance":[78],"evaluated":[80],"both":[82],"yield-space":[83],"price-space":[85],"predictions":[86],"at":[87,100],"1-day":[88],"5-day":[90],"horizons.":[91],"Empirically,":[92],"leads":[95],"strongest":[98],"improvements":[99],"short":[101],"maturities,":[102],"particularly":[103],"in":[104],"5-day-ahead":[105],"forecasts,":[106],"increasing":[107],"market-consistency":[108],"as":[109],"measured":[110],"bid-ask":[112],"hit":[113],"rates":[114],"reducing":[116],"dollar-denominated":[117],"prediction":[118],"errors.":[119]},"counts_by_year":[],"updated_date":"2025-11-27T01:16:37.896743","created_date":"2025-11-27T00:00:00"}
