Expected shortfall reliability—added value of traditional statistics and advanced artificial intelligence for market risk measurement purposes

The Fundamental Review of the Trading Book is a market risk measurement and management regulation recently issued by the Basel Committee. This reform, often referred to as “Basel IV”, intends to strengthen the financial system. The newest capital standard relies on the use of the Expected Shortfall....

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Detalles Bibliográficos
Autores: Carrillo Menéndez, Santiago, Hassani, Bertrand Kian
Tipo de recurso: artículo
Fecha de publicación:2021
País:España
Institución:Universidad Autónoma de Madrid
Repositorio:Biblos-e Archivo. Repositorio Institucional de la UAM
Idioma:inglés
OAI Identifier:oai:repositorio.uam.es:10486/705347
Acceso en línea:http://hdl.handle.net/10486/705347
https://dx.doi.org/10.3390/math9172142
Access Level:acceso abierto
Palabra clave:FRTB
GAN
SMOTE
Expected Shortfall
EM-Fittings
Market Risk
Matemáticas
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spelling Expected shortfall reliability—added value of traditional statistics and advanced artificial intelligence for market risk measurement purposesCarrillo Menéndez, SantiagoHassani, Bertrand KianFRTBGANSMOTEExpected ShortfallEM-FittingsMarket RiskMatemáticasThe Fundamental Review of the Trading Book is a market risk measurement and management regulation recently issued by the Basel Committee. This reform, often referred to as “Basel IV”, intends to strengthen the financial system. The newest capital standard relies on the use of the Expected Shortfall. This risk measure requires to get sufficient information in the tails to ensure its reliability, as this one has to be alimented by a sufficient quantity of relevant data (above the 97.5 percentile in the case of the regulation or interest). In this paper, after discussing the relevant features of Expected Shortfall for risk measurement purposes, we present and compare several methods allowing to ensure the reliability of the risk measure by generating information in the tails. We discuss these approaches with respect to their relevance considering the underlying situation when it comes to available data, allowing practitioners to select the most appropriate approach. We apply traditional statistical methodologies, for instance distribution fitting, kernel density estima-tion, Gaussian mixtures and conditional fitting by Expectation-Maximisation as well as AI related strategies, for instance a Synthetic Minority Over-sampling Technique implemented in a regression environment and Generative Adversarial NetsMDPIDepartamento de Matemáticas20212021-09-02research articlehttp://purl.org/coar/resource_type/c_2df8fbb1VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10486/705347https://dx.doi.org/10.3390/math9172142reponame:Biblos-e Archivo. Repositorio Institucional de la UAMinstname:Universidad Autónoma de MadridInglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:repositorio.uam.es:10486/7053472026-06-23T12:46:27Z
dc.title.none.fl_str_mv Expected shortfall reliability—added value of traditional statistics and advanced artificial intelligence for market risk measurement purposes
title Expected shortfall reliability—added value of traditional statistics and advanced artificial intelligence for market risk measurement purposes
spellingShingle Expected shortfall reliability—added value of traditional statistics and advanced artificial intelligence for market risk measurement purposes
Carrillo Menéndez, Santiago
FRTB
GAN
SMOTE
Expected Shortfall
EM-Fittings
Market Risk
Matemáticas
title_short Expected shortfall reliability—added value of traditional statistics and advanced artificial intelligence for market risk measurement purposes
title_full Expected shortfall reliability—added value of traditional statistics and advanced artificial intelligence for market risk measurement purposes
title_fullStr Expected shortfall reliability—added value of traditional statistics and advanced artificial intelligence for market risk measurement purposes
title_full_unstemmed Expected shortfall reliability—added value of traditional statistics and advanced artificial intelligence for market risk measurement purposes
title_sort Expected shortfall reliability—added value of traditional statistics and advanced artificial intelligence for market risk measurement purposes
dc.creator.none.fl_str_mv Carrillo Menéndez, Santiago
Hassani, Bertrand Kian
author Carrillo Menéndez, Santiago
author_facet Carrillo Menéndez, Santiago
Hassani, Bertrand Kian
author_role author
author2 Hassani, Bertrand Kian
author2_role author
dc.contributor.none.fl_str_mv Departamento de Matemáticas
dc.subject.none.fl_str_mv FRTB
GAN
SMOTE
Expected Shortfall
EM-Fittings
Market Risk
Matemáticas
topic FRTB
GAN
SMOTE
Expected Shortfall
EM-Fittings
Market Risk
Matemáticas
description The Fundamental Review of the Trading Book is a market risk measurement and management regulation recently issued by the Basel Committee. This reform, often referred to as “Basel IV”, intends to strengthen the financial system. The newest capital standard relies on the use of the Expected Shortfall. This risk measure requires to get sufficient information in the tails to ensure its reliability, as this one has to be alimented by a sufficient quantity of relevant data (above the 97.5 percentile in the case of the regulation or interest). In this paper, after discussing the relevant features of Expected Shortfall for risk measurement purposes, we present and compare several methods allowing to ensure the reliability of the risk measure by generating information in the tails. We discuss these approaches with respect to their relevance considering the underlying situation when it comes to available data, allowing practitioners to select the most appropriate approach. We apply traditional statistical methodologies, for instance distribution fitting, kernel density estima-tion, Gaussian mixtures and conditional fitting by Expectation-Maximisation as well as AI related strategies, for instance a Synthetic Minority Over-sampling Technique implemented in a regression environment and Generative Adversarial Nets
publishDate 2021
dc.date.none.fl_str_mv 2021
2021-09-02
dc.type.none.fl_str_mv research article
http://purl.org/coar/resource_type/c_2df8fbb1
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10486/705347
https://dx.doi.org/10.3390/math9172142
url http://hdl.handle.net/10486/705347
https://dx.doi.org/10.3390/math9172142
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:Biblos-e Archivo. Repositorio Institucional de la UAM
instname:Universidad Autónoma de Madrid
instname_str Universidad Autónoma de Madrid
reponame_str Biblos-e Archivo. Repositorio Institucional de la UAM
collection Biblos-e Archivo. Repositorio Institucional de la UAM
repository.name.fl_str_mv
repository.mail.fl_str_mv
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