Construction of a financial risk engineering model for banking supervision in the face of systemic crises

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The paper develops a model that improves the measurement of correlations present in stress scenarios through the use of copulas, reorders the propagation of shocks and involves expert judgments to improve predictions through a Bayesian VAR, it is shown that, under scenarios of a systemic crisis, los...

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Detalles Bibliográficos
Autor: Caparó, Rafael
Formato: artículo
Fecha de Publicación:2016
Institución:Universidad Nacional de Ingeniería
Repositorio:Revistas - Universidad Nacional de Ingeniería
Lenguaje:español
inglés
OAI Identifier:oai:oai:revistas.uni.edu.pe:article/1270
Enlace del recurso:https://revistas.uni.edu.pe/index.php/iecos/article/view/1270
Nivel de acceso:acceso abierto
Materia:modelo BVAR
posteriori
a priori
BVAR model
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spelling Construction of a financial risk engineering model for banking supervision in the face of systemic crisesConstrucción de un modelo de ingeniería del riesgo financiero para la supervisión bancaria frente a crisis sistémicasCaparó, Rafaelmodelo BVARposterioria prioriBVAR modelposterioria prioriThe paper develops a model that improves the measurement of correlations present in stress scenarios through the use of copulas, reorders the propagation of shocks and involves expert judgments to improve predictions through a Bayesian VAR, it is shown that, under scenarios of a systemic crisis, losses can reach high percentages. Considering a loss rate associated with counterparty default of 45% and a default threshold between 4% and 8% suggested by the Basel Committee on Banking Supervision, it is estimated that an external shock can generate falls of more than 10% in the different financial variables: the savings rate, stock market indexes, the exchange rate, among others. If a counterparty defaults, it generates 45% of the associated losses (exposure), each institution absorbs 45% of its exposures. The model constructed is applicable to regulatory agencies because it exposes a propagation mechanism through a financial contagion resulting from an external shock and subjected to stress tests. Taking into account the above assumptions, it is found that real variables can be affected by more than 15%. Although these rates are extreme and the stress scenario unlikely, it is necessary to consider these effects for the prevention of systemic crises, so it is advisable for regulatory authorities to emphasize the regulatory capital required from financial institutions.El documento desarrolla un modelo que mejora la medida de las correlaciones presentes en escenarios de estrés mediante el uso de cópulas, reordena la propagación de choques e involucra juicios de expertos para mejorar las predicciones mediante un VAR Bayesiano, se muestra que, bajo escenarios de una crisis sistémica, las pérdidas pueden alcanzar porcentajes  elevados. Si se considera una tasa de pérdida asociada al incumplimiento de la contraparte del 45% y un umbral de quiebra entre el 4% y 8% sugerido por el Comité de Supervisión Bancaria de Basilea, se estima que un choque externo puede generar caídas superiores al 10% en las diferentes variables financieras: la tasa de ahorro, los índices bursátiles, el tipo de cambio, entre otras. Si una contraparte entra en default genera un 45% de las pérdidas asociadas (exposición), cada institución absorbe el 45% de sus exposiciones. El modelo construido resulta de aplicabilidad para los organismos reguladores porque permite exponer un mecanismo de propagación a través de un contagio financiero resultante de un choque externo y sometido a pruebas de estrés. Teniendo en cuenta los supuestos mencionados, se encuentra que las  variables reales pueden ser afectadas en más del 15%. Aunque estas tasas son extremas y el escenario de estrés poco probable, es necesario considerar estos efectos para la prevención de crisis sistémicas, de tal manera que es aconsejable que las autoridades regulatorias pongan énfasis en el capital regulatorio exigido a las instituciones financieras.Universidad Nacional de Ingeniería2016-03-22info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionPeer ReviewedEvaluado por paresapplication/pdfaudio/mpegaudio/mpeghttps://revistas.uni.edu.pe/index.php/iecos/article/view/127010.21754/iecos.v17i0.1270revista IECOS; Vol. 17 (2016); 57-91Revista IECOS; Vol. 17 (2016); 57-912788-74802961-284510.21754/iecos.v17i0reponame:Revistas - Universidad Nacional de Ingenieríainstname:Universidad Nacional de Ingenieríainstacron:UNIspaenghttps://revistas.uni.edu.pe/index.php/iecos/article/view/1270/2769https://revistas.uni.edu.pe/index.php/iecos/article/view/1270/2770https://revistas.uni.edu.pe/index.php/iecos/article/view/1270/2771Derechos de autor 2016 Rafael Caparóhttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessoai:oai:revistas.uni.edu.pe:article/12702024-07-28T17:21:14Z
dc.title.none.fl_str_mv Construction of a financial risk engineering model for banking supervision in the face of systemic crises
Construcción de un modelo de ingeniería del riesgo financiero para la supervisión bancaria frente a crisis sistémicas
title Construction of a financial risk engineering model for banking supervision in the face of systemic crises
spellingShingle Construction of a financial risk engineering model for banking supervision in the face of systemic crises
Caparó, Rafael
modelo BVAR
posteriori
a priori
BVAR model
posteriori
a priori
title_short Construction of a financial risk engineering model for banking supervision in the face of systemic crises
title_full Construction of a financial risk engineering model for banking supervision in the face of systemic crises
title_fullStr Construction of a financial risk engineering model for banking supervision in the face of systemic crises
title_full_unstemmed Construction of a financial risk engineering model for banking supervision in the face of systemic crises
title_sort Construction of a financial risk engineering model for banking supervision in the face of systemic crises
dc.creator.none.fl_str_mv Caparó, Rafael
author Caparó, Rafael
author_facet Caparó, Rafael
author_role author
dc.subject.none.fl_str_mv modelo BVAR
posteriori
a priori
BVAR model
posteriori
a priori
topic modelo BVAR
posteriori
a priori
BVAR model
posteriori
a priori
description The paper develops a model that improves the measurement of correlations present in stress scenarios through the use of copulas, reorders the propagation of shocks and involves expert judgments to improve predictions through a Bayesian VAR, it is shown that, under scenarios of a systemic crisis, losses can reach high percentages. Considering a loss rate associated with counterparty default of 45% and a default threshold between 4% and 8% suggested by the Basel Committee on Banking Supervision, it is estimated that an external shock can generate falls of more than 10% in the different financial variables: the savings rate, stock market indexes, the exchange rate, among others. If a counterparty defaults, it generates 45% of the associated losses (exposure), each institution absorbs 45% of its exposures. The model constructed is applicable to regulatory agencies because it exposes a propagation mechanism through a financial contagion resulting from an external shock and subjected to stress tests. Taking into account the above assumptions, it is found that real variables can be affected by more than 15%. Although these rates are extreme and the stress scenario unlikely, it is necessary to consider these effects for the prevention of systemic crises, so it is advisable for regulatory authorities to emphasize the regulatory capital required from financial institutions.
publishDate 2016
dc.date.none.fl_str_mv 2016-03-22
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer Reviewed
Evaluado por pares
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://revistas.uni.edu.pe/index.php/iecos/article/view/1270
10.21754/iecos.v17i0.1270
url https://revistas.uni.edu.pe/index.php/iecos/article/view/1270
identifier_str_mv 10.21754/iecos.v17i0.1270
dc.language.none.fl_str_mv spa
eng
language spa
eng
dc.relation.none.fl_str_mv https://revistas.uni.edu.pe/index.php/iecos/article/view/1270/2769
https://revistas.uni.edu.pe/index.php/iecos/article/view/1270/2770
https://revistas.uni.edu.pe/index.php/iecos/article/view/1270/2771
dc.rights.none.fl_str_mv Derechos de autor 2016 Rafael Caparó
https://creativecommons.org/licenses/by/4.0
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Derechos de autor 2016 Rafael Caparó
https://creativecommons.org/licenses/by/4.0
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
audio/mpeg
audio/mpeg
dc.publisher.none.fl_str_mv Universidad Nacional de Ingeniería
publisher.none.fl_str_mv Universidad Nacional de Ingeniería
dc.source.none.fl_str_mv revista IECOS; Vol. 17 (2016); 57-91
Revista IECOS; Vol. 17 (2016); 57-91
2788-7480
2961-2845
10.21754/iecos.v17i0
reponame:Revistas - Universidad Nacional de Ingeniería
instname:Universidad Nacional de Ingeniería
instacron:UNI
instname_str Universidad Nacional de Ingeniería
instacron_str UNI
institution UNI
reponame_str Revistas - Universidad Nacional de Ingeniería
collection Revistas - Universidad Nacional de Ingeniería
repository.name.fl_str_mv
repository.mail.fl_str_mv
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