Bibliographic record
Abstract
Extreme risks associated with extraordinary market conditions are catastrophic for all investors. The ongoing financial crisis has perfectly exemplified this point. Surprisingly there are few studies exploring this issue for REITs. This study aims to close the knowledge gap. We conduct a comprehensive study by utilizing all three methodological categories to examine their forecasting performances of VaR and ES for nine major global REIT markets. Our findings indicate that there is no universally adequate method to model extreme risks across global markets. Also, estimating risks for the stock and REIT markets may require different methods. In addition, we compare the risk profiles between the stock and REIT markets, and find that the extreme risks for REITs are generally higher than those of stock markets. The fluctuations of risk levels are well synchronized between the two types of markets. The current crisis has significantly increased the extreme risk exposure for both REIT and stock investors. In all, our results have significant implications for REIT risk management, portfolio selection and evaluation.
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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 | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".