Risk Budgeting Allocation for Dynamic Risk Measures
Bibliographic record
Abstract
Time-Consistent Diversified Portfolio Allocations Portfolio allocation problems typically involve diversification of risks, which can be achieved by risk budgeting strategies. Of additional importance is being time consistent—that is, ensuring that decisions about what to do in the future under certain outcomes remain optimal when those future outcomes are realized. In “Risk Budgeting Allocation for Dynamic Risk Measures,” Pesenti, Jaimungal, Saporito, and Targino develop time-consistent portfolio strategies, termed dynamic risk budgeting strategies, where the diversification is on the level of risk contribution of assets. The authors formalize the dynamic setting of risk budgeting strategies, recast dynamic risk budgeting strategies as solutions of sequences of convex optimization problems, and develop an efficient actor-critic deep reinforcement learning algorithm for estimating dynamic risk budgeting strategies. The algorithm and the dynamic risk budgeting strategies are illustrated on a complex simulation case study involving five assets and 12 time steps.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".