High Volatility, Thick Tails and Extreme Value Theory in Value-at-Risk Estimation
Bibliographic record
Abstract
In this paper, the performance of the extreme value theory in Value-at-Risk calculations is compared to the performances of other well-known modeling techniques, such as GARCH, variance-covariance method and historical simulation in a volatile stock market. The models studied can be classified into two groups. The first group consists of GARCH(1,1) and GARCH(1,1)-t models which yield highly volatile quantile forecasts. The other group, consisting of historical simulation, variance-covariance approach, adaptive generalized pareto distribution (GPD) and nonadaptive GPD models leads to more stable quantile forecasts. The quantile forecasts of GARCH(1,1) models are excessively volatilite relative to the GPD quantile forecasts. This makes the GPD model to be a robust quantile forecasting tool which is practical to implement and regulate for VaR measurements. Key Words: Value-at-Risk, financial risk management, extreme value theory.
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How this classification was reachedexpand
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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".