The dynamics of risk premium: the case of the Taiwan real estate market
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose – This paper aims to examine the risk premium for investors in a changing information environment in the Taiwan, New York and London real estate markets from March 2006 to November 2014. This study attempts to quantify behavioral expectations regarding (or motivation for) investment in the Taiwanese real estate in a changing information environment. Design/methodology/approach – This paper uses the rolling generalised autoregressive conditionally heteroskedastic in mean (GARCH-M) methodology which fixes the problem of conventional GARCH-M methodology. Findings – Empirical evidence suggests that the time-varying risk premium changed for the Taiwan real estate market with a new information set. The risk premium changed from 1.305 per cent per month to −7.232 per cent per month. The study also found persistent volatility shocks from March 2006 to November 2014. No such evidence was found for the New York and London real estate markets. Overall, this study finds evidence of a time-varying risk premium, partly explainable by governmental policies and partly unexplainable. Research limitations/implications – The use of the index of Standard and Poor’s Taiwan Real Estate Investment Trusts to study the Taiwan real estate industry may have aggregation effects in result. Practical implications – The present study will provide guidance to investors as well as policymakers regarding the Taiwan real estate market. Originality/value – This study uses the rolling GARCH-M model, which is a first for the Taiwan real estate market.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it