Real estate as a dynamic risk in the financial sector: New international evidence using wavelet quantile correlation
Bibliographic record
Abstract
This article analyzes the role of real estate risks in the dynamics of financial sector stock returns for a sample of 14 countries: Asia and Oceania (Australia, Hong Kong, Japan, and Singapore), Europe (Belgium, France, Italy, Netherlands, Sweden, Switzerland and the U.K.) and North America (Canada and the USA). Real estate risk measures are drawn from the FTSE/EPRA NAREIT indexes. The period includes the last twenty years running from February 2005 to December 2024 on a daily and a monthly basis. The wavelet quantile correlation (WQC) methodology is implemented to highlight the impact of domestic and U.S. real estate risks. The WQC allows us to deal with time-varying characteristics of time series and to capture tail dependence. Besides, it has the advantage of dissolving the correlation structure between returns across different timescales. Our results report that the response to real estate risk pressures varies significantly depending on the financial sector, the investment horizon, and the origin of the real estate risk. The dynamic dimensions of the domestic and U.S. real estate risks during a long period, marked by significant crises including the Global financial crisis and the COVID-19 pandemic, are heterogeneous in the international financial sector, with potential implications for investment managers and policymakers.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".