On the Effect of Subprime Crisis on Value-at-Risk Estimation: GARCH Family Models Approach
Bibliographic record
Abstract
A survey of the risk management literature shows that few studies have attempted to take into account financial crisis in market risk measurement, in particular when using a Value-at Risk (VaR) analysis. In this paper, we use models to investigate the effects of subprime crisis on the Value-at-Risk estimation. In this framework, we investigate GARCH family models such as, GARCH, IGARCH, and GJR-GARCH. Each is adjusted based on three residuals distributions; normal, Student and Skewed Student-t. Using American stock market data, we show that dynamic volatility is different between the stability and during crisis periods. The estimation results indicate that the amount of VaR is different during these two time periods. This finding could be explained by the volatility clustering effect. The empirical results show also that GJR-GARCH model performs better in both sub-sample periods, in comparison with GARCH and IGARCH models. Moreover, we conclude that Student-t and Skewed Student-t distributions are preferred in the stable period while the normal distribution is recommended during the turbulent period.
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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 | 0.001 | 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 | 0.000 |
| 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".