Exploring a Three-Factor Dependence Structure of Conditional Volatilities: Some Quantile Regression Evidence from Real Estate Investment Trusts
Bibliographic record
Abstract
We propose a simple three-factor pricing model, consisting of a local stock market index, a global REIT market index, and a global stock market index, to examine the dependence structure of conditional volatilities in the real estate investment trust (REIT) market from 11 countries over the sample period from 1 June 2008 to 30 April 2021. The main quantile regression results reveal that a simultaneous dependence structure exists between each REIT market and local stock, global REIT market, and global stock market. There is a positive and significant dependence between REITs and three factors for every part of the quantiles. Across each quantile, Asia-Pacific REIT markets have a consistently higher average degree of dependence with their local stock markets than with the global stock and global REIT markets, whereas European REIT markets are generally more globally integrated. Furthermore, the lower and upper quantile estimates for over half of the REIT-quantiles for the three market factors are statistically different. Additionally, some REIT markets display asymmetric co-movement with at least one of the three factors as the degree of dependence increases when these markets are booming, but the dependence level declines when the markets are bearish. This evidence of dependence across the three influential factors and REIT markets provides meaningful insights into REIT market growth, international asset pricing, risk management, and dynamic linkages in the global economy.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".