REITs in a Mixed-Asset Portfolio:<i>An Investigation of Extreme Risks</i>
Bibliographic record
Abstract
Until the recent financial crisis, it was widely believed that adding real estate investment trusts (REITs) to a mixed-asset portfolio expanded the efficient frontier and provided superior risk-adjusted returns. More recent evidence suggests that REITs may have higher volatility, Value at Risk (VaR), and expected shortfall (ES) than equities in times of increased market volatility, precisely when the stabilizing properties of REITs are most desirable. This study expands on the emerging literature with two contributions. First, it examines the impact of REITs on the VaR of a portfolio of stocks and bonds over the last two decades. Second, a new, more accurate method of estimating VaR, Conditional Autoregressive Value at Risk (CAViaR), is used. The more accurate VaR estimates show that adding REITs to the portfolio has no significant impact on VaR until after the financial crisis begins. After the financial crisis begins, adding REITs to a portfolio of stocks and bonds dramatically increases VaR. The results have significant implications for portfolio selection. TOPICS:Real estate, mutual funds/passive investing/indexing, VAR and use of alternative risk measures of trading risk, portfolio construction
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".