INCORPORATION OF CONDITIONAL VALUE-AT-RISK INTO MEAN-VARIANCE OPTIMIZATION FOR PORTFOLIOS OF HEDGE FUNDS
Bibliographic record
Abstract
In this paper we aim to search for a systematic optimization model that can properlymeasure hedge fund risks and can optimize capital across Canadian hedge fund portfolios thatcan cater to investors’ risk appetites. As the characteristics of hedge funds returns imposedifferent layers of risk from traditional equity and bond investments, the conventional meanvarianceoptimization would not accurately capture the risk associated with non-normaldistributions and negative skewness. The process requires a different approach that modifiesthe drawback of a mean-variance optimization to take non-normal and asymmetricdistributions into consideration. The research of this process leads to a Mean-ConditionalValue-at-Risk (CVaR) optimization. CVaR measures the mean expected short fall betweenvalue-at-risk and excess losses that reflect the risks of kurtosis and negative skewness.Combining the cluster analysis to overcome variation of correlation issue and Mean-CVaRoptimization, we found the Mean-CVaR optimization model that will serve the requirementsof guiding investors’ capital allocation among hedge fund strategies.
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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 | 0.000 |
| 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".