{"id":"https://openalex.org/W7225575545","doi":"https://doi.org/10.5281/zenodo.24024954","title":"Parametric and Nonparametric Models of Joint Returns: Density, Dependence, and Portfolio Tail Risk","display_name":"Parametric and Nonparametric Models of Joint Returns: Density, Dependence, and Portfolio Tail Risk","publication_year":2026,"publication_date":"2026-11-07","ids":{"openalex":"https://openalex.org/W7225575545","doi":"https://doi.org/10.5281/zenodo.24024954"},"language":"en","primary_location":{"id":"doi:10.5281/zenodo.24024954","doi":"https://doi.org/10.5281/zenodo.24024954","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.24024954","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.5281/zenodo.24024954","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137392514","display_name":"Miquel Noguer Alonso","orcid":"https://orcid.org/0000-0002-4588-3594","observed_orcids":["https://orcid.org/0000-0002-4588-3594"]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Noguer i Alonso, Miquel","raw_affiliation_strings":["Artificial Intelligence Finance Institute"],"raw_orcid":"https://orcid.org/0000-0002-4588-3594","affiliations":[{"raw_affiliation_string":"Artificial Intelligence Finance Institute","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5137392514"],"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/T10282","display_name":"Financial Risk and Volatility Modeling","score":0.9940999746322632,"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":0.9940999746322632,"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":0.002199999988079071,"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":0.0019000000320374966,"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/copulas","display_name":"copulas","score":0.5709999799728394},{"id":"https://openalex.org/keywords/student-t-copula","display_name":"Student-t copula","score":0.7739999890327454},{"id":"https://openalex.org/keywords/filtered-historical-simulation","display_name":"filtered historical simulation","score":0.7749999761581421},{"id":"https://openalex.org/keywords/normalizing-flows","display_name":"normalizing flows","score":0.9300000071525574},{"id":"https://openalex.org/keywords/variational-autoencoder","display_name":"variational autoencoder","score":0.984000027179718},{"id":"https://openalex.org/keywords/wasserstein-gan","display_name":"Wasserstein GAN","score":0.996999979019165},{"id":"https://openalex.org/keywords/conditional-value-at-risk","display_name":"conditional value at risk","score":0.8199999928474426},{"id":"https://openalex.org/keywords/heston-model","display_name":"Heston model","score":0.6240000128746033},{"id":"https://openalex.org/keywords/neural-density-estimation","display_name":"neural density estimation","score":0.42899999022483826}],"concepts":[{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.6154999732971191},{"id":"https://openalex.org/C2780821815","wikidata":"https://www.wikidata.org/wiki/Q5340806","display_name":"Portfolio","level":2,"score":0.48410001397132874},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.4700999855995178},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.45980000495910645},{"id":"https://openalex.org/C91602232","wikidata":"https://www.wikidata.org/wiki/Q756115","display_name":"Volatility (finance)","level":2,"score":0.43619999289512634},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.40709999203681946},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4025000035762787},{"id":"https://openalex.org/C189508267","wikidata":"https://www.wikidata.org/wiki/Q17088227","display_name":"Density estimation","level":3,"score":0.36489999294281006},{"id":"https://openalex.org/C102366305","wikidata":"https://www.wikidata.org/wiki/Q1097688","display_name":"Nonparametric statistics","level":2,"score":0.3637000024318695},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.3553999960422516},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.33329999446868896},{"id":"https://openalex.org/C139819358","wikidata":"https://www.wikidata.org/wiki/Q462748","display_name":"Asset allocation","level":3,"score":0.3160000145435333},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3118000030517578},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.30970001220703125},{"id":"https://openalex.org/C18653775","wikidata":"https://www.wikidata.org/wiki/Q1333358","display_name":"Joint probability distribution","level":2,"score":0.30320000648498535},{"id":"https://openalex.org/C165216359","wikidata":"https://www.wikidata.org/wiki/Q670653","display_name":"Marginal distribution","level":3,"score":0.299699991941452},{"id":"https://openalex.org/C24574437","wikidata":"https://www.wikidata.org/wiki/Q7135228","display_name":"Parametric model","level":3,"score":0.2944999933242798},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.2930000126361847},{"id":"https://openalex.org/C107457265","wikidata":"https://www.wikidata.org/wiki/Q7546460","display_name":"Smoothing spline","level":4,"score":0.2883000075817108},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.2815000116825104},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2743000090122223},{"id":"https://openalex.org/C2778692605","wikidata":"https://www.wikidata.org/wiki/Q4041866","display_name":"Joins","level":2,"score":0.27250000834465027},{"id":"https://openalex.org/C96442724","wikidata":"https://www.wikidata.org/wiki/Q242188","display_name":"Invertible matrix","level":2,"score":0.26969999074935913},{"id":"https://openalex.org/C43555835","wikidata":"https://www.wikidata.org/wiki/Q2300258","display_name":"Conditional probability distribution","level":2,"score":0.2694000005722046},{"id":"https://openalex.org/C94128290","wikidata":"https://www.wikidata.org/wiki/Q963287","display_name":"Value at risk","level":3,"score":0.26510000228881836},{"id":"https://openalex.org/C202655437","wikidata":"https://www.wikidata.org/wiki/Q7231728","display_name":"Portfolio optimization","level":3,"score":0.2517000138759613}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.5281/zenodo.24024954","doi":"https://doi.org/10.5281/zenodo.24024954","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.24024954","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},{"id":"doi:10.5281/zenodo.24024955","doi":"https://doi.org/10.5281/zenodo.24024955","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.24024955","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.5281/zenodo.24024954","doi":"https://doi.org/10.5281/zenodo.24024954","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.24024954","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W7099518536","https://openalex.org/W6992835767","https://openalex.org/W1512824179","https://openalex.org/W4391790945","https://openalex.org/W7134923674","https://openalex.org/W3187826315","https://openalex.org/W2242179774","https://openalex.org/W7099860128","https://openalex.org/W7053114513","https://openalex.org/W7096464558"],"abstract_inverted_index":{"We":[0],"compare":[1],"parametric,":[2],"empirical":[3,45],"and":[4,19,25,58,66,80,84,91,159,187],"neural":[5,119],"models":[6],"of":[7],"joint":[8],"asset":[9],"returns":[10],"to":[11,191],"determine":[12],"whether":[13],"greater":[14],"flexibility":[15,158],"improves":[16],"density,":[17],"dependence":[18],"portfolio":[20],"risk.":[21],"Gaussian,":[22],"shrinkage-Gaussian,":[23],"Student":[24,34,38],"meta-t":[26,31,79],"laws":[27],"provide":[28],"parametric":[29],"benchmarks;":[30],"joins":[32],"asset-specific":[33],"margins":[35],"with":[36,97,147],"a":[37,122,170],"copula.":[39],"Filtered":[40],"historical":[41],"simulation":[42],"(FHS)":[43],"supplies":[44],"innovations.":[46],"Normalizing":[47],"flows":[48],"learn":[49,55,61],"invertible":[50],"density":[51,106],"transformations,":[52],"variational":[53],"autoencoders":[54],"latent-variable":[56],"distributions,":[57],"Wasserstein":[59],"GANs":[60],"scenario":[62],"generators.":[63],"Separate":[64],"volatility":[65],"correlation":[67],"controls":[68],"distinguish":[69,153],"conditioning":[70],"from":[71,156,163],"innovation":[72],"modeling.":[73],"Ten":[74],"estimation":[75],"datasets":[76],"per":[77],"Student,":[78],"Heston":[81],"synthetic":[82],"law,":[83],"2,514":[85],"CRSP":[86,110],"test":[87],"dates":[88],"in":[89,108],"five-basket":[90],"100-stock":[92,133],"panels,":[93],"support":[94],"paired":[95],"comparisons":[96],"multiplicity":[98],"control.":[99],"Meta-t":[100],"achieves":[101],"the":[102,137,143,178],"best":[103],"observed":[104],"exact":[105],"fit":[107],"both":[109],"panels.":[111],"Under":[112],"common":[113],"one-percent":[114],"validation":[115,188],"selection,":[116],"no":[117],"tested":[118],"model":[120],"establishes":[121],"risk-score":[123],"improvement":[124],"over":[125],"FHS;":[126],"alternative":[127],"checkpoint":[128],"rules":[129],"change":[130],"rankings.":[131],"Student's":[132],"advantage":[134],"depends":[135],"on":[136],"comparison":[138],"family.":[139],"Variance":[140],"filtering":[141],"produces":[142],"largest":[144],"point-score":[145],"gains,":[146],"inconclusive":[148],"adjusted":[149],"tests.":[150],"Synthetic":[151],"results":[152],"correct":[154],"specification":[155,186],"architectural":[157],"separate":[160],"likelihood":[161],"quality":[162],"expected-shortfall":[164],"accuracy.":[165],"A":[166],"Wishart\u2013CIR":[167],"extension":[168],"relaxes":[169],"restricted":[171],"covariance":[172],"model's":[173],"dispersion":[174],"ceiling":[175],"but":[176],"fails":[177],"conditional":[179],"numerical-stability":[180],"gate.":[181],"The":[182],"evidence":[183],"makes":[184],"conditioning,":[185],"design":[189],"central":[190],"assessing":[192],"distributional":[193],"flexibility.":[194]},"counts_by_year":[],"updated_date":"2026-11-10T11:39:43.021295","created_date":"2026-11-09T00:00:00"}