Investigation and Modelling of Economic Systematic Risk and Capital Requirement: A Monte Carlo Simulation
Bibliographic record
Abstract
This paper tests the ability of the regulatory capital requirement to cover credit losses at default, as carried out by the economic (optimal) capital requirement in Tunisian banks. The common factor in borrowers that leads to a credit default is systematic risk. However, the sensitivity to these factors differs between borrowers. To this end, we derived two kinds of sensitivity to systematic risk: the first is recognised by the Basel Committee; the second is derived from an economic approach. Hence, we can observe the impact of sensitivity to systematic risk on capital requirements. Empirically, we studied a sample of 100 individual borrowers from a Tunisian deposit bank that had credit in January 2020. We estimated the default probability for each borrower and then simulated their systematic risk sensitivity using the Monte Carlo approach, and compared them with the regulatory risk sensitivity. Then, we tested their effects on the economic and regulatory capital requirements. The results indicate that regulatory capital overestimates economic capital. This is due to the overestimation of borrowers’ contagion in terms of default risk, as shown by the superiority of their regulatory sensitivity systematic risk compared to the simulated risk. This leads banks to devote more capital than is really necessary to reach the regulatory standard. Hence, there was an increase in capital costs and the possibility of an arbitrage opportunity.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".