Long memory in REIT volatility revisited: genuine or spurious, and self-similar?
Bibliographic record
Abstract
This paper revisits the Real Estate Investment Trust (REIT) long-memory literature and addresses two important research questions: one, whether the observed long memory in REIT volatility is genuine or spurious (that is, caused by structural changes); and, two, a related one – whether the long memory is self-similar. Regarding the first question, we find strong evidence for the coexistence of pure long memory and structural breaks in all developed countries under study when daily data are used. But for the emerging markets under study some show coexistence while others show only pure long memory. Such a finding is also shared by both developed and emerging markets when it comes to using lower frequency data (weekly and monthly). As for the second question, we find support for self-similarity when we compare the daily and weekly long-memory estimates for the developed markets, implying that long memory is an intrinsic feature of the data. However, the support is not strong enough to completely rule out the possibility of structural breaks. Moreover, the support is found reduced when we consider the emerging markets and the monthly estimates from the developed markets. This is possibly due to the small sample size in both cases. Overall our findings have important implications for volatility modeling and forecasting.
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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 | 0.006 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".