Bibliographic record
Abstract
The literature is not clear on whether there are co-dependencies domestically across real estate and stock markets, despite the importance of this question for portfolio diversification strategies. In this article, we use fractional cointegration and long memory techniques to search for co-dependence in the Canadian markets. The measures of long-term persistence employed are the modified rescaled range statistic (R/S) proposed by Lo (1991), and the rescaled variance (V/S) statistic proposed by Giraitis et al. (2003). We find evidence to suggest long co-memories between stock and securitized property markets in the long term, but some evidence is also found in some sub-samples. The implication of our results is that securitized property and stocks are not considered to be substitutable assets over the short run and these assets may be held together in a portfolio for diversification purposes. However, over the long run, there is less benefit of holding both assets in a portfolio, since a fractional cointegration is found in the residual series.
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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) | 0.000 | 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 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".