{"id":"https://openalex.org/W2114033933","doi":"https://doi.org/10.1080/14697688.2014.926019","title":"Stress scenario selection by empirical likelihood","display_name":"Stress scenario selection by empirical likelihood","publication_year":2014,"publication_date":"2014-07-10","ids":{"openalex":"https://openalex.org/W2114033933","doi":"https://doi.org/10.1080/14697688.2014.926019","mag":"2114033933"},"language":"en","primary_location":{"id":"doi:10.1080/14697688.2014.926019","doi":"https://doi.org/10.1080/14697688.2014.926019","is_oa":false,"landing_page_url":"https://doi.org/10.1080/14697688.2014.926019","pdf_url":null,"source":{"id":"https://openalex.org/S182689569","display_name":"Quantitative Finance","issn_l":"1469-7688","issn":["1469-7688","1469-7696"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["abdc-a","anvur-class-a","anvur-scientific","cwts-core","dongbi-b","fnege-3","jufo-2","norway-1","poland-70","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547","https://openalex.org/P4310320449"],"host_organization_lineage_names":["Taylor & Francis","Informa"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Quantitative Finance","raw_type":"journal-article"},"volume":null,"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5026619574","display_name":"Paul Glasserman","orcid":"https://orcid.org/0000-0002-9577-0205","observed_orcids":["https://orcid.org/0000-0002-9577-0205"]},"institutions":[{"id":"https://openalex.org/I78577930","display_name":"Columbia University","ror":"https://ror.org/00hj8s172","country_code":"US","type":"education","lineage":["https://openalex.org/I78577930"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Paul Glasserman","raw_affiliation_strings":["Columbia Business School, Columbia University, New York, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Columbia Business School, Columbia University, New York, NY, USA","institution_ids":["https://openalex.org/I78577930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046387666","display_name":"Chulmin Kang","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4210158432","display_name":"National Institute for Mathematical Sciences","ror":"https://ror.org/04n7py080","country_code":"KR","type":"education","lineage":["https://openalex.org/I4210158432"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Chulmin Kang","raw_affiliation_strings":["National Institute for Mathematical Sciences, Daejeon, Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Institute for Mathematical Sciences, Daejeon, Republic of Korea","institution_ids":["https://openalex.org/I4210158432"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5018378125","display_name":"Wanmo Kang","orcid":"https://orcid.org/0000-0002-7145-921X","observed_orcids":["https://orcid.org/0000-0002-7145-921X"]},"institutions":[{"id":"https://openalex.org/I157485424","display_name":"Korea Advanced Institute of Science and Technology","ror":"https://ror.org/05apxxy63","country_code":"KR","type":"education","lineage":["https://openalex.org/I157485424"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Wanmo Kang","raw_affiliation_strings":["Department of Mathematical Sciences, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mathematical Sciences, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea","institution_ids":["https://openalex.org/I157485424"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5018378125"],"corresponding_institution_ids":["https://openalex.org/I157485424"],"apc_list":null,"apc_paid":null,"fwci":15.095,"has_fulltext":false,"cited_by_count":67,"citation_normalized_percentile":{"value":0.98855293,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"15","issue":"1","first_page":"25","last_page":"41"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10282","display_name":"Financial Risk and Volatility Modeling","score":0.8008000254631042,"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/T10282","display_name":"Financial Risk and Volatility Modeling","score":0.8008000254631042,"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/T11413","display_name":"Risk and Portfolio Optimization","score":0.052799999713897705,"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/T11720","display_name":"Probability and Risk Models","score":0.042899999767541885,"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/reverse-stress-testing","display_name":"reverse stress testing","score":0.8220000267028809},{"id":"https://openalex.org/keywords/empirical-likelihood","display_name":"empirical likelihood","score":0.8529999852180481},{"id":"https://openalex.org/keywords/conditional-expectation-estimation","display_name":"conditional mean estimation","score":0.8040000200271606},{"id":"https://openalex.org/keywords/confidence-regions","display_name":"confidence regions","score":0.6600000262260437},{"id":"https://openalex.org/keywords/elliptically-contoured-distributions","display_name":"elliptically contoured distributions","score":0.8140000104904175},{"id":"https://openalex.org/keywords/extreme-value-theory","display_name":"extreme value theory","score":0.6389999985694885},{"id":"https://openalex.org/keywords/regularly-varying-tails","display_name":"regularly varying tails","score":0.8410000205039978},{"id":"https://openalex.org/keywords/conditional-value-at-risk","display_name":"conditional value at risk","score":0.7870000004768372},{"id":"https://openalex.org/keywords/systemic-risk","display_name":"systemic risk","score":0.5230000019073486},{"id":"https://openalex.org/keywords/financial-risk-assessment","display_name":"financial risk assessment","score":0.8410000205039978},{"id":"https://openalex.org/keywords/portfolio-losses","display_name":"portfolio losses","score":0.36800000071525574}],"concepts":[{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.6072649955749512},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.5107576847076416},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.48368415236473083},{"id":"https://openalex.org/C21036866","wikidata":"https://www.wikidata.org/wiki/Q181767","display_name":"Stress (linguistics)","level":2,"score":0.42932984232902527},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.4207841455936432},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.33389613032341003},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.32078036665916443},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.30965861678123474},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.18182224035263062},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1080/14697688.2014.926019","doi":"https://doi.org/10.1080/14697688.2014.926019","is_oa":false,"landing_page_url":"https://doi.org/10.1080/14697688.2014.926019","pdf_url":null,"source":{"id":"https://openalex.org/S182689569","display_name":"Quantitative Finance","issn_l":"1469-7688","issn":["1469-7688","1469-7696"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["abdc-a","anvur-class-a","anvur-scientific","cwts-core","dongbi-b","fnege-3","jufo-2","norway-1","poland-70","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547","https://openalex.org/P4310320449"],"host_organization_lineage_names":["Taylor & Francis","Informa"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Quantitative Finance","raw_type":"journal-article"},{"id":"pmh:oai:RePEc:ofr:wpaper:13-04","doi":null,"is_oa":false,"landing_page_url":"https://www.financialresearch.gov/working-papers/files/OFRwp0007_GlassermanKangKang_StressScenarioSelectionbyEmpiricalLikelihood.pdf","pdf_url":"https://www.financialresearch.gov/working-papers/files/OFRwp0007_GlassermanKangKang_StressScenarioSelectionbyEmpiricalLikelihood.pdf","source":{"id":"https://openalex.org/S4306401271","display_name":"RePEc: Research Papers in Economics","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I77793887","host_organization_name":"Federal Reserve Bank of St. Louis","host_organization_lineage":["https://openalex.org/I77793887"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"preprint"},{"id":"pmh:oai:RePEc:taf:quantf:v:15:y:2015:i:1:p:25-41","doi":null,"is_oa":false,"landing_page_url":"http://hdl.handle.net/10.1080/14697688.2014.926019","pdf_url":null,"source":{"id":"https://openalex.org/S4306401271","display_name":"RePEc: Research Papers in Economics","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I77793887","host_organization_name":"Federal Reserve Bank of St. Louis","host_organization_lineage":["https://openalex.org/I77793887"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W184260756","https://openalex.org/W602946569","https://openalex.org/W2025760917","https://openalex.org/W2056664349","https://openalex.org/W2086730995","https://openalex.org/W2114111702","https://openalex.org/W2144568484","https://openalex.org/W2153712615","https://openalex.org/W2488562761","https://openalex.org/W3021145540","https://openalex.org/W3093773210","https://openalex.org/W4210688903","https://openalex.org/W4239200812","https://openalex.org/W4244841900","https://openalex.org/W4300246981"],"related_works":["https://openalex.org/W7099269708","https://openalex.org/W4407166356","https://openalex.org/W6942246944","https://openalex.org/W7164017950","https://openalex.org/W7166887859","https://openalex.org/W2093351163","https://openalex.org/W1999286169","https://openalex.org/W2336238343","https://openalex.org/W2304862073","https://openalex.org/W4242144221"],"abstract_inverted_index":{"This":[0],"paper":[1],"develops":[2],"a":[3,44,52,79,138,183],"method":[4],"for":[5,11,74,101,173],"selecting":[6],"and":[7,105,117,146,163,177],"analysing":[8],"stress":[9,32,205],"scenarios":[10,39],"financial":[12],"risk":[13,197],"assessment,":[14],"with":[15,190],"particular":[16],"emphasis":[17],"on":[18,30,83,182,200],"identifying":[19],"sensible":[20],"combinations":[21],"of":[22,57,61,86,113,128,131,134,141,159,195],"stresses":[23],"to":[24,41,90,152,171],"multiple":[25],"factors.":[26],"We":[27,47,69,97,123],"focus":[28],"primarily":[29],"reverse":[31],"testing":[33],"\u2013":[34],"finding":[35],"the":[36,58,62,75,84,87,92,102,106,110,114,126,129,132,135,157,160,178],"most":[37,93],"likely":[38,94],"leading":[40],"losses":[42],"exceeding":[43],"given":[45,66],"threshold.":[46],"approach":[48],"this":[49,153],"problem":[50],"using":[51],"nonparametric":[53],"empirical":[54,191],"likelihood":[55,192],"estimator":[56],"conditional":[59,76,161,181],"mean":[60,77],"underlying":[63],"market":[64,88,115,184],"factors":[65,89,116],"large":[67],"losses.":[68],"then":[70],"scale":[71],"confidence":[72,103],"regions":[73,104],"by":[78],"coefficient":[80],"that":[81],"depends":[82],"tails":[85,133,145],"estimate":[91],"loss":[95,119],"scenarios.":[96,206],"provide":[98],"rigorous":[99],"justification":[100],"scaling":[107],"procedure":[108],"when":[109],"joint":[111],"distribution":[112],"portfolio":[118],"is":[120],"elliptically":[121],"contoured.":[122],"explicitly":[124],"characterize":[125],"impact":[127],"heaviness":[130],"distribution,":[136],"contrasting":[137],"broad":[139],"spectrum":[140],"cases":[142],"including":[143],"exponential":[144],"regularly":[147],"varying":[148],"tails.":[149],"The":[150],"key":[151],"analysis":[154],"lies":[155],"in":[156,165,204],"asymptotics":[158,172],"variances":[162],"covariances":[164],"extremes.":[166],"These":[167],"results":[168,189],"also":[169],"lead":[170],"marginal":[174,201],"expected":[175,202],"shortfall":[176,203],"corresponding":[179],"variance,":[180],"stress;":[185],"we":[186],"combine":[187],"these":[188],"significance":[193],"tests":[194],"systemic":[196],"rankings":[198],"based":[199]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":7},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":6},{"year":2017,"cited_by_count":8},{"year":2016,"cited_by_count":8},{"year":2015,"cited_by_count":8},{"year":2014,"cited_by_count":1},{"year":2012,"cited_by_count":1}],"updated_date":"2026-11-03T10:50:19.383937","created_date":"2025-10-10T00:00:00"}
