A Multi-Objective Risk Return Trade off Models for Banks: Fuzzy Programming Approach
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Bibliographic record
Abstract
The main focus of banking sector is on the risk management. Asset liability management (ALM) is one of the key processes to manage the risks. The objective of this paper is to develop a multi-objective asset liability optimization model for banks with the maximization of market value of equity and minimization of duration gap as the objective function. Several liquidity ratios, concept of duration and convexity are considered to manage the risk properly. Interest rate risk and liquidity risk are two major considerations in both the regulation and management of a bank. As we know that, with the fluctuation of the market interest rate, the market value of assets and liabilities of a bank changes and that affects a change in owner’s equity. In order to overcome such type of situation here we will use the concept of duration and convexity to manage the interest rate risk. In case of liquidity risk the shortage of liquidity may also put that bank in risk and simultaneously it is very crucial to manage the cash flow properly. So here we will use some major liquidity ratios to manage the liquidity risk. We will take the help of fuzzy programming technique to solve our model properly. A numerical example is given to illustrate our model by considering a hypothetical bank balance sheet. Also we will compare the result obtained by fuzzy technique with result obtained by a non fuzzy based technique.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it