Portfolio risk dynamics: Managing value-at-risk across stocks and bonds
Bibliographic record
Abstract
Value-at-Risk (VaR) is a risk measurement metric used to mensurate the Economic Capital (EC), which consists of the capital at risk derived from investment activities. The formulation of risk management strategies is done through a pre-defined maximum target value for the EC. This dissertation measures and manages the VaR of a portfolio composed of equities and bonds from the European, U.S., Canadian and Asian markets with the aim of not exceeding a pre-defined target. Through a Backtest process, given the range of VaR models, 10 different models are analyzed by their performance, to select the model that provides the most thorough estimates for the portfolio. Selecting the best performing model, the VaR of the portfolio is measured daily and managed by an equity exposure hedging strategy for a one-year period, also capping the individual risk contribution of each asset for the total portfolio risk. Return on Risk-Adjusted Capital (RORAC) is a performance metric used to analyse the applied hedging strategy result.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".