Bibliographic record
Abstract
As an alternative investment class, REITs possess a unique ‘duality’ feature and have been experiencing strong downside movements recently. The paper aims to investigate a timely research subject on hedging downside risk for REITs. We consider a variety of downside risk measures and assess the hedging performance of minimum-downside-risk (MDR) strategies relative to the benchmark minimum-variance (MV) approach. Our study reveals two important findings that are distinctive from those reported in other markets: first, the MV approach leads to under-hedging compared with various MDR approaches. Second, a simpler historical simulation method generally outperforms the more complex Monte Carlo simulation method in estimating optimal hedge ratios. Our study also yields similar results as in other markets. We find that a decent amount of tail risk would still remain even after hedging whereas other types of down risk can be largely hedged away. Moreover, as the hedger becomes more concerned with tail risk, the hedging performance would deteriorate.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".