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On meeting capital requirements with a chance-constrained optimization model

This paper deals with a capital to risk asset ratio chance-constrained optimization model in the presence of loans, treasury bill, fixed assets and non-interest earning assets. To model the dynamics of loans, we introduce a modified CreditMetrics approach. This leads to development of a deterministi...

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Detalles Bibliográficos
Autores principales: Atta Mills, Ebenezer Fiifi Emire, Yu, Bo, Gu, Lanlan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4840157/
https://www.ncbi.nlm.nih.gov/pubmed/27186464
http://dx.doi.org/10.1186/s40064-016-2110-z
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author Atta Mills, Ebenezer Fiifi Emire
Yu, Bo
Gu, Lanlan
author_facet Atta Mills, Ebenezer Fiifi Emire
Yu, Bo
Gu, Lanlan
author_sort Atta Mills, Ebenezer Fiifi Emire
collection PubMed
description This paper deals with a capital to risk asset ratio chance-constrained optimization model in the presence of loans, treasury bill, fixed assets and non-interest earning assets. To model the dynamics of loans, we introduce a modified CreditMetrics approach. This leads to development of a deterministic convex counterpart of capital to risk asset ratio chance constraint. We pursue the scope of analyzing our model under the worst-case scenario i.e. loan default. The theoretical model is analyzed by applying numerical procedures, in order to administer valuable insights from a financial outlook. Our results suggest that, our capital to risk asset ratio chance-constrained optimization model guarantees banks of meeting capital requirements of Basel III with a likelihood of 95 % irrespective of changes in future market value of assets.
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spelling pubmed-48401572016-05-16 On meeting capital requirements with a chance-constrained optimization model Atta Mills, Ebenezer Fiifi Emire Yu, Bo Gu, Lanlan Springerplus Research This paper deals with a capital to risk asset ratio chance-constrained optimization model in the presence of loans, treasury bill, fixed assets and non-interest earning assets. To model the dynamics of loans, we introduce a modified CreditMetrics approach. This leads to development of a deterministic convex counterpart of capital to risk asset ratio chance constraint. We pursue the scope of analyzing our model under the worst-case scenario i.e. loan default. The theoretical model is analyzed by applying numerical procedures, in order to administer valuable insights from a financial outlook. Our results suggest that, our capital to risk asset ratio chance-constrained optimization model guarantees banks of meeting capital requirements of Basel III with a likelihood of 95 % irrespective of changes in future market value of assets. Springer International Publishing 2016-04-22 /pmc/articles/PMC4840157/ /pubmed/27186464 http://dx.doi.org/10.1186/s40064-016-2110-z Text en © Atta Mills et al. 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Research
Atta Mills, Ebenezer Fiifi Emire
Yu, Bo
Gu, Lanlan
On meeting capital requirements with a chance-constrained optimization model
title On meeting capital requirements with a chance-constrained optimization model
title_full On meeting capital requirements with a chance-constrained optimization model
title_fullStr On meeting capital requirements with a chance-constrained optimization model
title_full_unstemmed On meeting capital requirements with a chance-constrained optimization model
title_short On meeting capital requirements with a chance-constrained optimization model
title_sort on meeting capital requirements with a chance-constrained optimization model
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4840157/
https://www.ncbi.nlm.nih.gov/pubmed/27186464
http://dx.doi.org/10.1186/s40064-016-2110-z
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