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On meeting capital requirements with a chance-constrained optimization model

Overview of attention for article published in SpringerPlus, April 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (89th percentile)
  • High Attention Score compared to outputs of the same age and source (94th percentile)

Mentioned by

twitter
20 tweeters

Citations

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7 Dimensions

Readers on

mendeley
11 Mendeley
Title
On meeting capital requirements with a chance-constrained optimization model
Published in
SpringerPlus, April 2016
DOI 10.1186/s40064-016-2110-z
Pubmed ID
Authors

Ebenezer Fiifi Emire Atta Mills, Bo Yu, Lanlan Gu

Abstract

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.

Twitter Demographics

The data shown below were collected from the profiles of 20 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 11 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
China 1 9%
Unknown 10 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 18%
Researcher 2 18%
Student > Bachelor 1 9%
Student > Master 1 9%
Other 1 9%
Other 0 0%
Unknown 4 36%
Readers by discipline Count As %
Economics, Econometrics and Finance 2 18%
Computer Science 1 9%
Business, Management and Accounting 1 9%
Social Sciences 1 9%
Medicine and Dentistry 1 9%
Other 0 0%
Unknown 5 45%

Attention Score in Context

This research output has an Altmetric Attention Score of 18. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 21 May 2022.
All research outputs
#1,785,677
of 23,005,189 outputs
Outputs from SpringerPlus
#103
of 1,855 outputs
Outputs of similar age
#31,207
of 299,476 outputs
Outputs of similar age from SpringerPlus
#9
of 148 outputs
Altmetric has tracked 23,005,189 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,855 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.7. This one has done particularly well, scoring higher than 94% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 299,476 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 89% of its contemporaries.
We're also able to compare this research output to 148 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 94% of its contemporaries.