{"id":"https://openalex.org/W4416195045","doi":"https://doi.org/10.1145/3768292.3771250","title":"Leveraging Deep Learning Optimization for Monte Carlo Calibration of (Rough) Stochastic Volatility Models","display_name":"Leveraging Deep Learning Optimization for Monte Carlo Calibration of (Rough) Stochastic Volatility Models","publication_year":2025,"publication_date":"2025-11-14","ids":{"openalex":"https://openalex.org/W4416195045","doi":"https://doi.org/10.1145/3768292.3771250"},"language":null,"primary_location":{"id":"doi:10.1145/3768292.3771250","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3768292.3771250","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 6th ACM International Conference on AI in Finance","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3768292.3771250","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5069340033","display_name":"Lukas Gonon","orcid":"https://orcid.org/0000-0003-3367-2455"},"institutions":[{"id":"https://openalex.org/I202963720","display_name":"University of St. Gallen","ror":"https://ror.org/0561a3s31","country_code":"CH","type":"education","lineage":["https://openalex.org/I202963720"]}],"countries":["CH"],"is_corresponding":true,"raw_author_name":"Lukas Gonon","raw_affiliation_strings":["University of St. Gallen, St. Gallen, Switzerland"],"affiliations":[{"raw_affiliation_string":"University of St. Gallen, St. Gallen, Switzerland","institution_ids":["https://openalex.org/I202963720"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5032754635","display_name":"Wolfgang Stockinger","orcid":"https://orcid.org/0000-0002-5305-7786"},"institutions":[{"id":"https://openalex.org/I47508984","display_name":"Imperial College London","ror":"https://ror.org/041kmwe10","country_code":"GB","type":"education","lineage":["https://openalex.org/I47508984"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Wolfgang Stockinger","raw_affiliation_strings":["Imperial College London, London, United Kingdom"],"affiliations":[{"raw_affiliation_string":"Imperial College London, London, United Kingdom","institution_ids":["https://openalex.org/I47508984"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5069340033"],"corresponding_institution_ids":["https://openalex.org/I202963720"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.41603527,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"779","last_page":"787"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11413","display_name":"Risk and Portfolio Optimization","score":0.37869998812675476,"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/T11413","display_name":"Risk and Portfolio Optimization","score":0.37869998812675476,"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/T10067","display_name":"Stochastic processes and financial applications","score":0.08020000159740448,"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.060499999672174454,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.8187000155448914},{"id":"https://openalex.org/keywords/calibration","display_name":"Calibration","score":0.5787000060081482},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5454000234603882},{"id":"https://openalex.org/keywords/hybrid-monte-carlo","display_name":"Hybrid Monte Carlo","score":0.46880000829696655},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.46650001406669617},{"id":"https://openalex.org/keywords/automatic-differentiation","display_name":"Automatic differentiation","score":0.43070000410079956},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.3483000099658966},{"id":"https://openalex.org/keywords/stochastic-volatility","display_name":"Stochastic volatility","score":0.3262999951839447}],"concepts":[{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.8187000155448914},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6728000044822693},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.5787000060081482},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5454000234603882},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.507099986076355},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4715000092983246},{"id":"https://openalex.org/C13153151","wikidata":"https://www.wikidata.org/wiki/Q1639846","display_name":"Hybrid Monte Carlo","level":4,"score":0.46880000829696655},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.46650001406669617},{"id":"https://openalex.org/C133512626","wikidata":"https://www.wikidata.org/wiki/Q787371","display_name":"Automatic differentiation","level":3,"score":0.43070000410079956},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4244000017642975},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.3483000099658966},{"id":"https://openalex.org/C85393063","wikidata":"https://www.wikidata.org/wiki/Q596307","display_name":"Stochastic volatility","level":3,"score":0.3262999951839447},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3224000036716461},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.3091000020503998},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.2978000044822693},{"id":"https://openalex.org/C37669827","wikidata":"https://www.wikidata.org/wiki/Q6904703","display_name":"Monte Carlo molecular modeling","level":4,"score":0.296999990940094},{"id":"https://openalex.org/C63320529","wikidata":"https://www.wikidata.org/wiki/Q7269435","display_name":"Quasi-Monte Carlo method","level":5,"score":0.28049999475479126},{"id":"https://openalex.org/C132725507","wikidata":"https://www.wikidata.org/wiki/Q39879","display_name":"Monte Carlo integration","level":5,"score":0.27900001406669617},{"id":"https://openalex.org/C206688291","wikidata":"https://www.wikidata.org/wiki/Q7617819","display_name":"Stochastic gradient descent","level":3,"score":0.2775000035762787},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.2705000042915344},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.26759999990463257},{"id":"https://openalex.org/C91602232","wikidata":"https://www.wikidata.org/wiki/Q756115","display_name":"Volatility (finance)","level":2,"score":0.2662000060081482},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.26429998874664307}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3768292.3771250","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3768292.3771250","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 6th ACM International Conference on AI in Finance","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3768292.3771250","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3768292.3771250","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 6th ACM International Conference on AI in Finance","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":44,"referenced_works":["https://openalex.org/W1603849743","https://openalex.org/W1972375231","https://openalex.org/W2118284477","https://openalex.org/W2477011206","https://openalex.org/W2588426171","https://openalex.org/W2595720639","https://openalex.org/W2605753999","https://openalex.org/W2625974289","https://openalex.org/W2744090841","https://openalex.org/W2902318560","https://openalex.org/W2962802749","https://openalex.org/W2963562494","https://openalex.org/W2971672318","https://openalex.org/W3019596641","https://openalex.org/W3021131124","https://openalex.org/W3023610427","https://openalex.org/W3095372661","https://openalex.org/W3109759108","https://openalex.org/W3122362998","https://openalex.org/W3122662177","https://openalex.org/W3124875579","https://openalex.org/W3126083004","https://openalex.org/W3126103175","https://openalex.org/W3135707115","https://openalex.org/W3153771038","https://openalex.org/W3167120613","https://openalex.org/W3169451066","https://openalex.org/W3182382065","https://openalex.org/W4236558617","https://openalex.org/W4247451115","https://openalex.org/W4283011320","https://openalex.org/W4303685646","https://openalex.org/W4306962685","https://openalex.org/W4381955100","https://openalex.org/W4382047356","https://openalex.org/W4385635455","https://openalex.org/W4387127404","https://openalex.org/W4388015318","https://openalex.org/W4389883284","https://openalex.org/W4391368157","https://openalex.org/W4392000350","https://openalex.org/W4401684229","https://openalex.org/W4404369631","https://openalex.org/W4415877397"],"related_works":[],"abstract_inverted_index":{"Model":[0],"calibration":[1,14,20,29,73],"is":[2,15,42],"a":[3,34,75],"central":[4],"task":[5],"faced":[6],"by":[7,18,123],"financial":[8,97],"services":[9],"institutions.":[10],"Accurate":[11],"and":[12,88,102],"fast":[13],"typically":[16],"ensured":[17],"hard-coding":[19],"routines":[21],"individually":[22],"for":[23,93,113],"different":[24],"models.":[25,38],"Alternatively,":[26],"Monte":[27,65,71,114],"Carlo":[28,66,72,115],"can":[30],"be":[31],"applied":[32],"to":[33,84,96,129],"wide":[35],"range":[36],"of":[37,81,121],"Typically,":[39],"parameter":[40],"optimization":[41,90],"performed":[43],"using":[44],"potentially":[45],"noisy":[46],"approximate":[47],"gradient":[48],"computations":[49],"or":[50],"exact":[51],"gradients":[52],"computed":[53],"via":[54],"adjoint":[55],"algorithmic":[56],"differentiation":[57],"(AAD).":[58],"In":[59],"this":[60],"paper":[61],"we":[62],"introduce":[63],"Automatic":[64],"Calibration":[67],"(AMCC),":[68],"which":[69],"formulates":[70],"as":[74],"deep":[76,94,103],"learning":[77,95,104],"problem.":[78],"This":[79],"point":[80],"view":[82],"allows":[83],"readily":[85],"apply":[86],"backpropagation":[87],"gradient-based":[89],"schemes":[91],"developed":[92],"model":[98],"calibration,":[99],"bridging":[100],"AAD":[101],"approaches.":[105],"Thereby,":[106],"AMCC":[107,122],"leverages":[108],"computational":[109],"graph":[110],"structure-based":[111],"implementations":[112],"calibration.":[116],"We":[117],"showcase":[118],"the":[119],"flexibility":[120],"calibrating":[124],"rough":[125],"stochastic":[126],"volatility":[127],"models":[128],"S&P":[130],"500":[131],"Index":[132],"options":[133],"data.":[134]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-11-14T00:00:00"}
