Is Risk Based Capital Ratio a True Measure of the Soundness of Banks? Evidence From India
Bibliographic record
Abstract
The capital to risk-weighted assets ratio (CRAR) introduced by the Basel Committee on Banking Supervision (BCBS) of the Bank for International Settlement (BIS) is widely used as an important measure of the soundness of banks. However, international institutions, academics, and market analytics have increasingly questioned the reliability of the ratio which has been revised a number of times since its inception, to make it more robust. The main objective of the present study is to assess the adequacy of using CRAR as a measure of the soundness of banks. One of the most popular methods used for the analysis of the banks’ financial soundness is CAMELS framework which uses six key dimensions: capital adequacy, asset quality, management quality, earning ability, liquidity, and sensitivity to market risk. Before the introduction of CRAR, the ratio of capital to the total assets was widely used as a measure of capital adequacy in the CAMELS framework. However, when risk-based CRAR was introduced, banking regulators and policymakers started using it in the CAMELS framework for representing capital adequacy. In this paper, we argue that CRAR, as a comprehensive forward-looking measure, encapsulates all the six dimensions of the financial soundness of banks, representing various facets of banking operations, which are covered in the CAMELS framework. We, therefore, develop a theoretical framework to establish the relationship between risk-based CRAR and the important ratios used under the CAMELS framework and then empirically investigate the relationship using a panel regression model using data of Indian banks for the period 2009–2018. The results indicate that CRAR has a significant and theoretically consistent relationship with all six dimensions of the CAMEL framework. The study, therefore, does not only confirm the appropriateness of using CRAR as a measure of the soundness of banks, but also opens up a debate on whether CRAR can be an alternative for the CAMELS framework.
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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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".