| 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. |
X Demographics
The data shown below were collected from the profiles of 19 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Ghana | 7 | 37% |
| United States | 1 | 5% |
| Belgium | 1 | 5% |
| Unknown | 10 | 53% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 17 | 89% |
| Practitioners (doctors, other healthcare professionals) | 1 | 5% |
| Scientists | 1 | 5% |
Mendeley readers
The data shown below were compiled from readership statistics for 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| China | 1 | 8% |
| Unknown | 11 | 92% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 2 | 17% |
| Researcher | 2 | 17% |
| Student > Bachelor | 1 | 8% |
| Student > Master | 1 | 8% |
| Other | 1 | 8% |
| Other | 0 | 0% |
| Unknown | 5 | 42% |
| Readers by discipline | Count | As % |
|---|---|---|
| Economics, Econometrics and Finance | 2 | 17% |
| Computer Science | 1 | 8% |
| Business, Management and Accounting | 1 | 8% |
| Social Sciences | 1 | 8% |
| Medicine and Dentistry | 1 | 8% |
| Other | 0 | 0% |
| Unknown | 6 | 50% |
Attention Score in Context
This research output has an Altmetric Attention Score of 17. 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
#2,204,451
of 25,754,670 outputs
Outputs from SpringerPlus
#119
of 1,876 outputs
Outputs of similar age
#34,659
of 314,197 outputs
Outputs of similar age from SpringerPlus
#11
of 149 outputs
Altmetric has tracked 25,754,670 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,876 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.2. This one has done particularly well, scoring higher than 93% of its peers.
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We're also able to compare this research output to 149 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 92% of its contemporaries.