The Quantification of Systemic Risk and Stability: New Methods and Measures
Bibliographic record
Abstract
We address the question of the prediction of large failures, busts, or system collapse, and the necessary concepts related to risk quantification, minimization and management.Answering this question requires a new approach since predictions using standard financial techniques and statistical distributions fail to predict or anticipate crises.The key points are that financial markets, systems, trading and manoeuvres are not just about money, debt, stocks, instruments and assets but reflect the actions and motivations of humans, which includes the presence or absence of learning effects.Therefore we have the possibility of failures or rare or low frequency events due to human involvement.The rare or unknown event is directly due to human influence, and reflects both learning and risk taking, with the presence of the finite and persistent human error contribution while taking or exposed to risk.This presence of humans in the marketplace explains the failure of present purely statistical methods to correctly estimate, predict or determine the onset of financial crises, busts and collapses.In this essay, we unify the concepts for predicting financial systemic risk with the general theory for outcomes, trends and measures already derived for other technical and social systems with human involvement.We replace words and qualitative reasoning with measures and quantitative predictions.The paper is therefore written with an introductory section devoted to the measures relevant to risk prediction in other modern technological systems; and is then extended and applied specifically to risk prediction for financial and business systems.The resulting measures also provide useful guidance for risk governance.Romney B.
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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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".