Optimal Portfolio Allocation Using Funds of Hedge Funds
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 paper compares different methods of optimization for a portfolio allocation that includes funds of funds. Optimization consists of minimizing risk measured by one of the following proxies: normal Value at Risk (VaR), adjusted VaR (adjusted using the Cornish-Fisher expansion), weighted historical simulation VaR, and semi-deviation. Results indicate that compared to the other proxies of VaR, normal VaR tends to underestimate portfolio risk. Moreover funds of funds improve the risk-return profile of the portfolio. This last result is interesting since funds of hedge funds exhibit less of the individual hedge funds' biases reported in the literature. <bold>TOPICS:</bold> <ext-link>Real assets/alternative investments/private equity</ext-link>, <ext-link>VAR and use of alternative risk measures of trading risk</ext-link>, <ext-link>statistical methods</ext-link>, <ext-link>portfolio construction</ext-link>
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it