Bibliographic record
Abstract
<p>This thesis studies two risk measurement methods, Value-at-Risk (VaR) method and Conditional-Value-at-Risk (CVaR) method. The concepts, prop- erties and calculation methods of VaR and CVaR method are introduced. On the basis of CVaR method, the mean-CVaR model is established. The thesis focuses on modeling a dynamic CVaR portfolio optimization problem based on the dynamic programming method. Moreover, a VaR constraint is added to the model, which strengthens the dynamic CVaR portfolio optimization. Through numerical analysis, the investment risk loss value and the portfolio investment ratio under the relevant confidence level can be obtained. From the results based on the real stock data, it is concluded that the risk of multi-stage portfolio investment is much smaller than that of single-stage investment. Finally, two other methods were selected for comparison. In summary, the CVaR method has a considerable rate of return and moderate risk. The thesis is wrapped up with a conclusion and future work.</p>
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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 | 0.000 | 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 | 0.001 |
| 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".