Value optimisation in a regulatory constrained regime — A new look at risk vs return optimisation
Bibliographic record
Abstract
Basel II made Pillar I's regulatory capital (RC) more risk-sensitive and brought it closer to economic capital (EC). In many financial institutions (FIs), RC is close to, or even larger than, EC. Constrained by RC, many FIs have been using RC in addition to, or as a replacement for, EC for capital allocation, performance management and even for pricing/deal acceptance to ensure a sufficient return is provided to shareholders. Although Basel II's RC provides a much better estimation of risk relative to Basel I, it still comes short in many areas—most notably in concentration and diversification risk which is essential for value optimisation. Owing to its ability to more accurately capture concentration and diversification, its larger coverage and overall better accuracy, EC should be the primary metric in risk return optimisation for the entire bank. RC, on the other hand, is a regulatory reality and a very strong constraint which is likely to be stronger with the implementation of Basel III. In this paper value optimisation in a regulatory constrained regime is examined. An optimisation model is proposed to allocate both EC and RC to FIs’ business units so that net economic profit for the entire bank is maximised subject to constraints. An empirical study is conducted and the results are interpreted. It was found that there are significant differences in net economic profit (NEP) and return on capital (ROC) among alternative risk and growth strategies which can be recognised by using an optimisation model which can be easily operationalised. FIs over-focusing on net income (NI) without a proper optimisation framework in their annual planning exercise are unlikely to determine and to be able to take advantage of these value maximising opportunities. It is also clear that the existence of RC affects the optimisation problem significantly and creates economic cost even when at the total bank level total RC is less than the total EC requirement. With Basel III implementation, if RC, as expected, becomes more of a binding constraint, the systemic inefficiency and thus the economic cost will further increase. Significant value can be created by increasing confidence in EC and by not imposing RC as a binding constraint.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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 source (direct Gemma or distilled Codex), 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".