Hedging geopolitical risks with real estate investments : evidence from the Covid-19 pandemic
Bibliographic record
Abstract
This study investigates the potential of Real Estate Investment Trusts (REITs) as hedging instruments against geopolitical and physical risks, focusing on their performance during the COVID19 pandemic. Using a comprehensive dataset from June 2014 to May 2024, the analysis incorporates daily data on REIT indices across seven regions alongside proxies for geopolitical risks (COVOL and GPR) and physical climate risks (e.g., hurricanes and global warming). Quantitative methods, including regression models, VAR, GARCH, and risk-adjusted metrics, evaluate REITs' sensitivity to these risks. The findings reveal significant regional disparities. While US and European REITs demonstrate resilience, regions like Canada, Australia, and China exhibit heightened sensitivity to geopolitical volatility. Interaction effects indicate that physical risks amplify geopolitical impacts, especially in areas prone to extreme weather events. The COVID19 pandemic exacerbated these dynamics, with geopolitical risks intensifying the adverse effects on REIT returns globally, particularly in Canada, Australia, and China. The results highlight REITs' potential as diversification tools, especially in developed markets, while cautioning against higher volatility in emerging markets. These insights contribute to portfolio diversification strategies and emphasize the importance of institutional resilience and climate risk mitigation. This research advances the understanding of REITs' performance under compounded crises, addressing a critical gap in the literature on alternative hedging mechanisms.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".