Machine learning for liquidity risk modelling

Detalhes bibliográficos
Autor(a) principal: Guerra, Pedro
Data de Publicação: 2022
Outros Autores: Castelli, Mauro, Côrte-real, Nadine
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/133346
Resumo: Guerra, P., Castelli, M., & Côrte-real, N. (2022). Machine learning for liquidity risk modelling: A supervisory perspective. Economic Analysis and Policy, 74(June), 175-187. https://doi.org/10.1016/j.eap.2022.02.001
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spelling Machine learning for liquidity risk modellingA supervisory perspectiveBanking supervisionRisk assessmentMachine learningEWSLiquidityScenario analysisECB risk assessment systemEconomics and EconometricsEconomics, Econometrics and Finance (miscellaneous)SDG 16 - Peace, Justice and Strong InstitutionsSDG 8 - Decent Work and Economic GrowthGuerra, P., Castelli, M., & Côrte-real, N. (2022). Machine learning for liquidity risk modelling: A supervisory perspective. Economic Analysis and Policy, 74(June), 175-187. https://doi.org/10.1016/j.eap.2022.02.001The purpose of an effective liquidity risk assessment policy is to ensure that any given credit institution can meet its cash flow obligations, even factoring in the uncertainty caused by external factors. As part of the Supervisory Review and Evaluation Process (SREP), the European Central Bank (ECB) has determined this assessment should take into consideration both the institution’s ability to meet its short-term obligations and its long-term funding strategy. Due to the fast pace of financial markets and more demanding regulations, there is a structural need for a precise and widely accepted risk assessment methodology. Furthermore, the ability to foresee alternative scenarios by stressing the involved key risk indicators is of the utmost importance. This work investigates whether machine learning techniques can successfully model liquidity risk, thus providing insights for stress-testing scenarios. We have applied the Risk Assessment System (RAS) methodology to classify credit institutions from the Portuguese banking sector according to their liquidity risk, using real supervisory data (from 2014 until March 2021). We then studied the ability to model this risk classification, by comparing a series of well-established machine learning algorithms to a traditional statistical model for benchmarking. The results show that extreme gradient boosting (XGBoost) outperforms other methods for this classification problem. The resulting model can be set up for a production environment and provide scenarios for stress-testing, or as an early warning system (EWS), thus supporting the overall SREP exercise.NOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNGuerra, PedroCastelli, MauroCôrte-real, Nadine2024-02-19T01:30:48Z2022-06-012022-06-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article13application/pdfhttp://hdl.handle.net/10362/133346eng1538-0653PURE: 41915728https://doi.org/10.1016/j.eap.2022.02.001info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:11:57Zoai:run.unl.pt:10362/133346Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:47:45.825568Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Machine learning for liquidity risk modelling
A supervisory perspective
title Machine learning for liquidity risk modelling
spellingShingle Machine learning for liquidity risk modelling
Guerra, Pedro
Banking supervision
Risk assessment
Machine learning
EWS
Liquidity
Scenario analysis
ECB risk assessment system
Economics and Econometrics
Economics, Econometrics and Finance (miscellaneous)
SDG 16 - Peace, Justice and Strong Institutions
SDG 8 - Decent Work and Economic Growth
title_short Machine learning for liquidity risk modelling
title_full Machine learning for liquidity risk modelling
title_fullStr Machine learning for liquidity risk modelling
title_full_unstemmed Machine learning for liquidity risk modelling
title_sort Machine learning for liquidity risk modelling
author Guerra, Pedro
author_facet Guerra, Pedro
Castelli, Mauro
Côrte-real, Nadine
author_role author
author2 Castelli, Mauro
Côrte-real, Nadine
author2_role author
author
dc.contributor.none.fl_str_mv NOVA Information Management School (NOVA IMS)
Information Management Research Center (MagIC) - NOVA Information Management School
RUN
dc.contributor.author.fl_str_mv Guerra, Pedro
Castelli, Mauro
Côrte-real, Nadine
dc.subject.por.fl_str_mv Banking supervision
Risk assessment
Machine learning
EWS
Liquidity
Scenario analysis
ECB risk assessment system
Economics and Econometrics
Economics, Econometrics and Finance (miscellaneous)
SDG 16 - Peace, Justice and Strong Institutions
SDG 8 - Decent Work and Economic Growth
topic Banking supervision
Risk assessment
Machine learning
EWS
Liquidity
Scenario analysis
ECB risk assessment system
Economics and Econometrics
Economics, Econometrics and Finance (miscellaneous)
SDG 16 - Peace, Justice and Strong Institutions
SDG 8 - Decent Work and Economic Growth
description Guerra, P., Castelli, M., & Côrte-real, N. (2022). Machine learning for liquidity risk modelling: A supervisory perspective. Economic Analysis and Policy, 74(June), 175-187. https://doi.org/10.1016/j.eap.2022.02.001
publishDate 2022
dc.date.none.fl_str_mv 2022-06-01
2022-06-01T00:00:00Z
2024-02-19T01:30:48Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/133346
url http://hdl.handle.net/10362/133346
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1538-0653
PURE: 41915728
https://doi.org/10.1016/j.eap.2022.02.001
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 13
application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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