{"id":"https://openalex.org/W4415002934","doi":"https://doi.org/10.1109/access.2025.3619553","title":"GAN-Enhanced Implied Volatility Surface Reconstruction for Option Pricing Error Mitigation","display_name":"GAN-Enhanced Implied Volatility Surface Reconstruction for Option Pricing Error Mitigation","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4415002934","doi":"https://doi.org/10.1109/access.2025.3619553"},"language":"en","primary_location":{"id":"doi:10.1109/access.2025.3619553","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3619553","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2025.3619553","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101940769","display_name":"Yao Ge","orcid":"https://orcid.org/0000-0001-6314-3891"},"institutions":[{"id":"https://openalex.org/I145608581","display_name":"University of Miami","ror":"https://ror.org/02dgjyy92","country_code":"US","type":"education","lineage":["https://openalex.org/I145608581"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yao Ge","raw_affiliation_strings":["1University of Miami, Miami, FL, USA","University of Miami, Miami, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"1University of Miami, Miami, FL, USA","institution_ids":["https://openalex.org/I145608581"]},{"raw_affiliation_string":"University of Miami, Miami, USA","institution_ids":["https://openalex.org/I145608581"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Ying Wang","orcid":"https://orcid.org/0009-0007-5474-3503"},"institutions":[{"id":"https://openalex.org/I100191712","display_name":"Pepperdine University","ror":"https://ror.org/0529ybh43","country_code":"US","type":"education","lineage":["https://openalex.org/I100191712"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ying Wang","raw_affiliation_strings":["2Pepperdine University, Malibu, CA, USA","Pepperdine University, Malibu, USA"],"raw_orcid":"https://orcid.org/0009-0007-5474-3503","affiliations":[{"raw_affiliation_string":"2Pepperdine University, Malibu, CA, USA","institution_ids":["https://openalex.org/I100191712"]},{"raw_affiliation_string":"Pepperdine University, Malibu, USA","institution_ids":["https://openalex.org/I100191712"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064714374","display_name":"Jingyi Liu","orcid":"https://orcid.org/0009-0001-8075-6478"},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]},{"id":"https://openalex.org/I87424562","display_name":"Ithaca College","ror":"https://ror.org/01kw1gj07","country_code":"US","type":"education","lineage":["https://openalex.org/I87424562"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jingyi Liu","raw_affiliation_strings":["3Cornell University, Ithaca, NY, USA","Cornell University, Ithaca, USA"],"raw_orcid":"https://orcid.org/0009-0001-8075-6478","affiliations":[{"raw_affiliation_string":"3Cornell University, Ithaca, NY, USA","institution_ids":["https://openalex.org/I87424562","https://openalex.org/I205783295"]},{"raw_affiliation_string":"Cornell University, Ithaca, USA","institution_ids":["https://openalex.org/I205783295"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5070177593","display_name":"Jiyuan Wang","orcid":"https://orcid.org/0009-0009-7957-777X"},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jiyuan Wang","raw_affiliation_strings":["4Duke University, Durham, NC, USA","Duke University, Durham, USA"],"raw_orcid":"https://orcid.org/0009-0009-7957-777X","affiliations":[{"raw_affiliation_string":"4Duke University, Durham, NC, USA","institution_ids":["https://openalex.org/I170897317"]},{"raw_affiliation_string":"Duke University, Durham, USA","institution_ids":["https://openalex.org/I170897317"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":16.8639,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.99360265,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"13","issue":null,"first_page":"176770","last_page":"176787"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10067","display_name":"Stochastic processes and financial applications","score":0.9858999848365784,"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"}},"topics":[{"id":"https://openalex.org/T10067","display_name":"Stochastic processes and financial applications","score":0.9858999848365784,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/implied-volatility","display_name":"Implied volatility","score":0.7505999803543091},{"id":"https://openalex.org/keywords/volatility-smile","display_name":"Volatility smile","score":0.7084000110626221},{"id":"https://openalex.org/keywords/valuation-of-options","display_name":"Valuation of options","score":0.6262000203132629},{"id":"https://openalex.org/keywords/volatility","display_name":"Volatility (finance)","score":0.5343999862670898},{"id":"https://openalex.org/keywords/stochastic-volatility","display_name":"Stochastic volatility","score":0.5260999798774719},{"id":"https://openalex.org/keywords/moneyness","display_name":"Moneyness","score":0.46709999442100525},{"id":"https://openalex.org/keywords/sabr-volatility-model","display_name":"SABR volatility model","score":0.4480000138282776},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.3790999948978424}],"concepts":[{"id":"https://openalex.org/C24189920","wikidata":"https://www.wikidata.org/wiki/Q1660345","display_name":"Implied volatility","level":3,"score":0.7505999803543091},{"id":"https://openalex.org/C13290067","wikidata":"https://www.wikidata.org/wiki/Q915788","display_name":"Volatility smile","level":3,"score":0.7084000110626221},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6500999927520752},{"id":"https://openalex.org/C194483076","wikidata":"https://www.wikidata.org/wiki/Q380382","display_name":"Valuation of options","level":2,"score":0.6262000203132629},{"id":"https://openalex.org/C91602232","wikidata":"https://www.wikidata.org/wiki/Q756115","display_name":"Volatility (finance)","level":2,"score":0.5343999862670898},{"id":"https://openalex.org/C85393063","wikidata":"https://www.wikidata.org/wiki/Q596307","display_name":"Stochastic volatility","level":3,"score":0.5260999798774719},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.4713999927043915},{"id":"https://openalex.org/C173921975","wikidata":"https://www.wikidata.org/wiki/Q1806850","display_name":"Moneyness","level":2,"score":0.46709999442100525},{"id":"https://openalex.org/C187625094","wikidata":"https://www.wikidata.org/wiki/Q7388452","display_name":"SABR volatility model","level":4,"score":0.4480000138282776},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.3790999948978424},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.36320000886917114},{"id":"https://openalex.org/C32896092","wikidata":"https://www.wikidata.org/wiki/Q189447","display_name":"Risk management","level":2,"score":0.3474999964237213},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.33489999175071716},{"id":"https://openalex.org/C19244329","wikidata":"https://www.wikidata.org/wiki/Q208697","display_name":"Financial market","level":2,"score":0.32429999113082886},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.30469998717308044},{"id":"https://openalex.org/C163128081","wikidata":"https://www.wikidata.org/wiki/Q1338307","display_name":"Black\u2013Scholes model","level":3,"score":0.29019999504089355},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.2896000146865845},{"id":"https://openalex.org/C114118609","wikidata":"https://www.wikidata.org/wiki/Q3036837","display_name":"Market data","level":2,"score":0.2743000090122223},{"id":"https://openalex.org/C2779834249","wikidata":"https://www.wikidata.org/wiki/Q6664586","display_name":"Local volatility","level":4,"score":0.2676999866962433},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.26440000534057617},{"id":"https://openalex.org/C117996083","wikidata":"https://www.wikidata.org/wiki/Q5473296","display_name":"Forward volatility","level":4,"score":0.25780001282691956}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2025.3619553","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3619553","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:e2c72d22f22b419cb42adf81d11b2842","is_oa":true,"landing_page_url":"https://doaj.org/article/e2c72d22f22b419cb42adf81d11b2842","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 13, Pp 176770-176787 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2025.3619553","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3619553","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0,101,185,229],"accurate":[1],"modeling":[2],"of":[3,52,97],"implied":[4,71],"volatility":[5,53,72,91],"surfaces":[6,73,92,115],"is":[7],"crucial":[8],"for":[9,166,194,221,236],"option":[10,77],"pricing":[11,147,192],"and":[12,69,108,169,216,226,241],"risk":[13,239],"management":[14],"in":[15,43,48,145,160,173,191,244],"financial":[16],"markets.":[17],"Traditional":[18],"parametric":[19,99],"approaches,":[20],"such":[21],"as":[22],"the":[23,38,49,83,95,113,150,181,203],"Stochastic":[24],"Volatility":[25],"Inspired":[26],"(SVI)":[27],"model,":[28],"often":[29],"suffer":[30],"from":[31,75],"rigid":[32],"functional":[33],"forms":[34],"that":[35,93,137],"inadequately":[36],"capture":[37],"complex":[39],"nonlinear":[40],"patterns":[41],"observed":[42],"real":[44],"market":[45,78,214],"data,":[46],"particularly":[47,188],"tail":[50],"regions":[51],"smiles.":[54],"This":[55],"paper":[56],"introduces":[57],"a":[58,157,170],"novel":[59],"Generative":[60],"Adversarial":[61],"Network":[62],"(GAN)":[63],"framework":[64],"specifically":[65],"designed":[66],"to":[67,87,111,180],"learn":[68],"reconstruct":[70],"directly":[74],"historical":[76],"prices.":[79],"Our":[80],"approach":[81,140,208],"leverages":[82],"adversarial":[84],"training":[85],"mechanism":[86],"generate":[88],"smooth,":[89],"market-consistent":[90],"overcome":[94],"limitations":[96],"conventional":[98],"models.":[100],"proposed":[102,230],"GAN":[103],"architecture":[104],"incorporates":[105],"domain-specific":[106],"constraints":[107],"regularization":[109],"techniques":[110],"ensure":[112],"generated":[114],"satisfy":[116],"fundamental":[117],"no-arbitrage":[118],"conditions":[119],"while":[120],"maintaining":[121],"computational":[122],"efficiency.":[123],"Through":[124],"comprehensive":[125],"empirical":[126],"evaluation":[127],"using":[128],"real-world":[129],"AAPL":[130],"options":[131,168,222],"data":[132],"spanning":[133],"2016-2020,":[134],"we":[135],"demonstrate":[136],"our":[138,154,207],"GAN-based":[139],"significantly":[141],"outperforms":[142],"traditional":[143,199],"methods":[144],"reducing":[146],"errors":[148],"under":[149],"Black-Scholes":[151],"framework.":[152],"Specifically,":[153],"method":[155],"achieves":[156],"23.7%":[158],"reduction":[159],"mean":[161,175],"absolute":[162],"percentage":[163],"error":[164,177],"(MAPE)":[165],"out-of-the-money":[167,196],"31.4%":[171],"improvement":[172],"root":[174],"square":[176],"(RMSE)":[178],"compared":[179],"baseline":[182],"SVI":[183],"model.":[184],"results":[186],"indicate":[187],"substantial":[189],"improvements":[190],"accuracy":[193],"deep":[195],"options,":[197],"where":[198],"models":[200],"typically":[201],"exhibit":[202],"largest":[204],"deviations.":[205],"Furthermore,":[206],"demonstrates":[209],"superior":[210],"stability":[211],"across":[212],"different":[213],"regimes":[215],"provides":[217],"enhanced":[218],"generalization":[219],"capabilities":[220],"with":[223],"varying":[224],"maturities":[225],"moneyness":[227],"levels.":[228],"methodology":[231],"offers":[232],"significant":[233],"practical":[234],"implications":[235],"derivatives":[237],"trading,":[238],"management,":[240],"model":[242],"validation":[243],"quantitative":[245],"finance.":[246]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":7}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}