On the Use of Long-Term Risk Measures as an Approach to Communicating Risks
Bibliographic record
Abstract
Abstract Value at risk (VaR) is a widely used measure for financial risks. However, as argued in Taleb (2012), “VaR encourages low volatility, high blowup risk taking which can be gamed by the Wall Street bonus structure.” It was also argued that one reason for this is the limited ability of all quantitative risk measures (including VaR, TVaR and many other modifications) to measure the risk of extreme events (black swans). In this paper, we argue that VaR and its modifications, being short–term in nature, intend to measure extreme risk by creating extreme small probability values. Even if accurate, they might not be effective in communicating risk to people because it is well documented in the psychology literature that humans tend to make irrational decisions when dealing with extreme small probabilities. As such, we propose that long-term risk measures, such as ruin probabilities over a long time horizon, provide a natural approach to avoid small probability values in measuring the risk of extreme events. They could be considered as a vehicle to communicate extreme risks to fund managers, insurance companies, as well as the public, and to help them in making decisions under uncertainty.
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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 | 0.017 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".