Flight to quality and portfolio diversification under ambiguity of correlation
Why this work is in the frame
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Bibliographic record
Abstract
We argue that ambiguous correlation between asset payoffs plays an important role in the occurrence of “flight to quality”, which in some circumstances leads investors with incomplete information to portfolio under-diversification. In this paper, we consider a multi-asset economy with four types of investors who have heterogeneous beliefs on correlation coefficients, and in which ambiguity-averse traders make decisions in a maxmin expected utility framework. A unique general equilibrium presents in four scenarios according to the dispersion of asset quality. We define a measure to gauge the degree of portfolio under-diversification, with which we show that correlation ambiguity will drive less-informed investors to hold a nondiversified portfolio if the correlation coefficient is negative, while a positive correlation drives some less-informed investors to hold a fully diversified portfolio.
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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 | 0.000 | 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 it