Value at Risk, Legislative Framework, Crises, and Procyclicality: what goes wrong?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study highlights some deficiencies of the stock markets’ risk legislation framework, and particularly the CESR (2010) guidelines. We show that the current legislative framework fails to offer incentives to financial management companies to invest in advanced models for more representative Value at Risk (VaR) estimations, and for this reason, in many cases conventional VaR models are applied. We use data from the DAX, CAC 40, FTSE, FTSEMIB and IBEX indices, and then we apply them to the widely accepted Delta Normal VaR model. The empirical findings show that the conventional VaR models not only fail to provide information for the upcoming financial crises, but also contribute to such phenomena as procyclicality and overreaction in the stock market. We suggest additional tests and we empirically show how these tests could reduce the procyclicality issue and promote a more sustainable investment environment. Even though this study is mainly focused on CESR (2010) guidelines, it could be useful for any similar legislative framework, such as the Basel Accords.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it