Measuring the risk of an Iranian banking system using Value at Risk (VaR) Model
Bibliographic record
Abstract
Measuring risk of financial institutes and banks plays an important role on managing them. Recent financial turmoil in United States banking system has motivated banking industry to monitor risk factors more closely. In this paper, we present an empirical study to measure the risk of some private banks in Iran called Bank Mellat using Value at Risk (VaR) method. The proposed study collects the necessary information for the fiscal year of 2010 and analyses them using regression analysis. The study divides the financial data into two groups where the financial data of the first half of year is considered in the first group and the remaining information for the second half of year 2010 is considered in the second group. The implementation of VaR method indicates that financial risks increase during the time horizon. The study also uses linear regression method where independent variable is time, dependent variable is the financial risk, and the results confirm what we have found in the previous part of the survey.
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How this classification was reachedexpand
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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".