The Impact of the Global Financial Crisis on the Co-Integration Relationship between Reit and Stock Markets: A Dynamic Co-Integration Approach
Bibliographic record
Abstract
The aim of this paper is to analyze the impact of the Global Financial Crisis (GFC) on the co-integration relationship between the REIT and stock market indices using a sample of 10 developed countries. The main tool employed for this purpose is the dynamic co-integration approach. The empirical results strongly suggest that the stock and REIT markets were deeply affected by two successive crises. The first crisis was related to the U.S. subprime problems while the second shock emanated from the European insolvency problems. The shocks led to serious structural breaks in the financial data during the 2007-2012 period. As a result of this and the highly variable nature of the co-integration structure during this period, the conventional and static Johansen tests cannot detect the strong co-integration between the REIT and stock markets which were the result of common negative response of both markets to the successive shocks. Dynamic co-integration approach seems to be a more valid tool to capture the dynamics of the co-integration structure after the GFC. The dynamic approach implies that the destruction of diversification benefits between the REIT and stock markets was essentially a shock related outcome which also implies that the diversification potential between these two markets may still be valid in the absence of shocks.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".