{"id":"https://openalex.org/W4404351472","doi":"https://doi.org/10.1145/3677052.3698668","title":"EX-DRL: Hedging Against Heavy Losses with EXtreme Distributional Reinforcement Learning","display_name":"EX-DRL: Hedging Against Heavy Losses with EXtreme Distributional Reinforcement Learning","publication_year":2024,"publication_date":"2024-11-14","ids":{"openalex":"https://openalex.org/W4404351472","doi":"https://doi.org/10.1145/3677052.3698668"},"language":"en","primary_location":{"id":"doi:10.1145/3677052.3698668","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3677052.3698668","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3677052.3698668","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 5th 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://dl.acm.org/doi/pdf/10.1145/3677052.3698668","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5017386905","display_name":"Parvin Malekzadeh","orcid":null},"institutions":[{"id":"https://openalex.org/I185261750","display_name":"University of Toronto","ror":"https://ror.org/03dbr7087","country_code":"CA","type":"education","lineage":["https://openalex.org/I185261750"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Parvin Malekzadeh","raw_affiliation_strings":["University of Toronto, Canada"],"raw_orcid":"https://orcid.org/0000-0003-2533-232X","affiliations":[{"raw_affiliation_string":"University of Toronto, Canada","institution_ids":["https://openalex.org/I185261750"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018312650","display_name":"Zissis Poulos","orcid":"https://orcid.org/0000-0002-2427-7413"},"institutions":[{"id":"https://openalex.org/I192455969","display_name":"York University","ror":"https://ror.org/05fq50484","country_code":"CA","type":"education","lineage":["https://openalex.org/I192455969"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Zissis Poulos","raw_affiliation_strings":["York University, Canada"],"raw_orcid":"https://orcid.org/0000-0002-2427-7413","affiliations":[{"raw_affiliation_string":"York University, Canada","institution_ids":["https://openalex.org/I192455969"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Jacky Chen","orcid":"https://orcid.org/0009-0004-8528-220X"},"institutions":[{"id":"https://openalex.org/I185261750","display_name":"University of Toronto","ror":"https://ror.org/03dbr7087","country_code":"CA","type":"education","lineage":["https://openalex.org/I185261750"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Jacky Chen","raw_affiliation_strings":["University of Toronto, Canada"],"raw_orcid":"https://orcid.org/0009-0004-8528-220X","affiliations":[{"raw_affiliation_string":"University of Toronto, Canada","institution_ids":["https://openalex.org/I185261750"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Zeyu Wang","orcid":"https://orcid.org/0009-0001-4796-7476"},"institutions":[{"id":"https://openalex.org/I185261750","display_name":"University of Toronto","ror":"https://ror.org/03dbr7087","country_code":"CA","type":"education","lineage":["https://openalex.org/I185261750"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Zeyu Wang","raw_affiliation_strings":["University of Toronto, Canada"],"raw_orcid":"https://orcid.org/0009-0001-4796-7476","affiliations":[{"raw_affiliation_string":"University of Toronto, Canada","institution_ids":["https://openalex.org/I185261750"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5059152392","display_name":"Konstantinos N. Plataniotis","orcid":"https://orcid.org/0000-0003-3647-5473"},"institutions":[{"id":"https://openalex.org/I185261750","display_name":"University of Toronto","ror":"https://ror.org/03dbr7087","country_code":"CA","type":"education","lineage":["https://openalex.org/I185261750"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Konstantinos N Plataniotis","raw_affiliation_strings":["University of Toronto, Canada"],"raw_orcid":"https://orcid.org/0000-0003-3647-5473","affiliations":[{"raw_affiliation_string":"University of Toronto, Canada","institution_ids":["https://openalex.org/I185261750"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5017386905"],"corresponding_institution_ids":["https://openalex.org/I185261750"],"apc_list":null,"apc_paid":null,"fwci":1.4079,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":{"value":0.84044153,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"370","last_page":"378"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11413","display_name":"Risk and Portfolio Optimization","score":0.9796000123023987,"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":0.9796000123023987,"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.9491999745368958,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10067","display_name":"Stochastic processes and financial applications","score":0.9416000247001648,"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/reinforcement-learning","display_name":"Reinforcement learning","score":0.7947341203689575},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5018784999847412},{"id":"https://openalex.org/keywords/reinforcement","display_name":"Reinforcement","score":0.46946799755096436},{"id":"https://openalex.org/keywords/environmental-science","display_name":"Environmental science","score":0.32576221227645874},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.26988619565963745},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1646992266178131},{"id":"https://openalex.org/keywords/structural-engineering","display_name":"Structural engineering","score":0.07798883318901062}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7947341203689575},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5018784999847412},{"id":"https://openalex.org/C67203356","wikidata":"https://www.wikidata.org/wiki/Q1321905","display_name":"Reinforcement","level":2,"score":0.46946799755096436},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.32576221227645874},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.26988619565963745},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1646992266178131},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.07798883318901062}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3677052.3698668","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3677052.3698668","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3677052.3698668","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 5th ACM International Conference on AI in Finance","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3677052.3698668","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3677052.3698668","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3677052.3698668","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 5th ACM International Conference on AI in Finance","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4404351472.pdf","grobid_xml":"https://content.openalex.org/works/W4404351472.grobid-xml"},"referenced_works_count":26,"referenced_works":["https://openalex.org/W2075090252","https://openalex.org/W2077791698","https://openalex.org/W2765302304","https://openalex.org/W2914584772","https://openalex.org/W2992360057","https://openalex.org/W2995616440","https://openalex.org/W3017191956","https://openalex.org/W3018556014","https://openalex.org/W3041107988","https://openalex.org/W3081043352","https://openalex.org/W3130784248","https://openalex.org/W3162544201","https://openalex.org/W3166964686","https://openalex.org/W3169505047","https://openalex.org/W3175709607","https://openalex.org/W3213633080","https://openalex.org/W3217410169","https://openalex.org/W4247451115","https://openalex.org/W4306317474","https://openalex.org/W4306962685","https://openalex.org/W4321497586","https://openalex.org/W4372183490","https://openalex.org/W4384807683","https://openalex.org/W4388092788","https://openalex.org/W4388994420","https://openalex.org/W4392909880"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W4310083477","https://openalex.org/W2328553770","https://openalex.org/W2920061524","https://openalex.org/W1977959518","https://openalex.org/W2038908348","https://openalex.org/W2107890255","https://openalex.org/W2106552856"],"abstract_inverted_index":{"Recent":[0],"advancements":[1],"in":[2,14,18,24,45,79,88,102],"Distributional":[3],"Reinforcement":[4],"Learning":[5],"(DRL)":[6],"for":[7,182],"modeling":[8,120],"loss":[9,31,81,125],"distributions":[10,32],"have":[11],"shown":[12],"promise":[13],"developing":[15],"hedging":[16,47,156],"strategies":[17],"derivatives":[19],"markets.":[20],"A":[21],"common":[22],"approach":[23],"DRL":[25,90,112],"involves":[26],"learning":[27],"the":[28,73,80,93,103,121,124,140,175],"quantiles":[29,78],"of":[30,76,97,123,142,170,179],"at":[33,58,64],"specified":[34],"levels":[35],"using":[36],"Quantile":[37],"Regression":[38],"(QR).":[39],"This":[40,133],"method":[41,134],"is":[42,189],"particularly":[43],"effective":[44],"option":[46],"due":[48,91],"to":[49,92,138],"its":[50],"direct":[51],"quantile-based":[52],"risk":[53,69,180,185],"assessment,":[54],"such":[55],"as":[56,100],"Value":[57,63],"Risk":[59,65],"(VaR)":[60],"and":[61,95,177],"Conditional":[62],"(CVaR).":[66],"However,":[67],"these":[68],"measures":[70],"depend":[71],"on":[72,154],"accurate":[74],"estimation":[75,148],"extreme":[77,116,143,171],"distribution\u2019s":[82],"tail,":[83],"which":[84,114],"can":[85],"be":[86],"imprecise":[87],"QR-based":[89,163],"rarity":[94],"extremity":[96],"tail":[98,122],"data,":[99],"highlighted":[101],"literature.":[104],"To":[105],"address":[106],"this":[107],"issue,":[108],"we":[109],"propose":[110],"EXtreme":[111],"(EX-DRL),":[113],"enhances":[115],"quantile":[117,144],"prediction":[118],"by":[119,165],"distribution":[126],"with":[127],"a":[128],"Generalized":[129],"Pareto":[130],"Distribution":[131],"(GPD).":[132],"introduces":[135],"supplementary":[136],"data":[137],"mitigate":[139],"scarcity":[141],"observations,":[145],"thereby":[146,173],"improving":[147,174],"accuracy":[149],"through":[150],"QR.":[151],"Comprehensive":[152],"experiments":[153],"gamma":[155],"options":[157],"demonstrate":[158],"that":[159],"EX-DRL":[160],"improves":[161],"existing":[162],"models":[164],"providing":[166],"more":[167],"precise":[168],"estimates":[169],"quantiles,":[172],"computation":[176],"reliability":[178],"metrics":[181],"complex":[183],"financial":[184],"management.":[186],"The":[187],"implementation":[188],"available":[190],"here.":[191]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}