Measuring systemic risk: A financial statement–based approach for insurance firms and banks
Bibliographic record
Abstract
Abstract We introduce CRISK, a financial statement–based measure, to assess the systemic risk contribution of a financial firm. CRISK measures the capital shortfall of a financial firm conditional on severe distress in the entire system. Our measure complements the market‐based measure, SRISK, introduced by Acharya et al. (2012, American Economic Review , 102 (3), 59–64) and Brownlees and Engle (2017, Review of Financial Studies , 30 (1), 48–79), in identifying systemically risky financial firms. While SRISK provides a timelier assessment using real‐time stock market data, CRISK offers a more nuanced approach using accounting information and is tailored to the distinct characteristics of insurance firms and commercial banks. Our empirical analysis shows that (1) compared to CRISK, SRISK tends to overestimate capital shortfalls for insurance firms and for banks that hold a substantial portion of Federal Deposit Insurance Corporation–insured deposits while underestimating capital shortfalls for banks heavily reliant on uninsured deposits; (2) CRISK estimates of capital shortfall closely align with the actual capital injections received by financial firms during the financial crisis of 2007–2009; and (3) CRISK exhibits a significant positive correlation with short interest. Based on our findings, we recommend using SRISK as an initial screening tool to identify potential systemically risky financial firms, followed by refining the list and validating the expected capital shortfall using CRISK.
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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 | 0.008 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".