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Record W2033076897 · doi:10.1108/02637470910946390

The asymmetric volatility of house prices in the UK

2009· article· en· W2033076897 on OpenAlex

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueProperty Management · 2009
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing Market and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsVolatility (finance)HeteroscedasticityEconomicsReal estateHouse priceEconometricsAutoregressive conditional heteroskedasticityQuarter (Canadian coin)Financial economicsFinance

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to show an indication that the asymmetric volatility between house price movement may account for the defensiveness of the housing market. Design/methodology/approach First the UK nation‐wide house price data from the last quarter (Q4) of 1955 to the last quarter of 2005 are used and then the most suitable mean and variance equations to estimate the conditional heteroscedasticity volatilities of the returns of house prices are selected. Second, a variable that examines the leverage effect of volatility is incorporated into the model. The GJR‐GARCH model is used. Findings The results of the empirical test show that while the lagged innovations are negatively correlated with housing return, that is when there is bad news, the current volatility of housing return might decline. Research limitations/implications The results indicate that the volatilities between house prices moving up and moving down are asymmetric. Practical implications The results show that there is a defensive effect in the UK housing market during the data periods used. Originality/value Although several articles have documented that there is heteroscedasticity and autocorrelation in the volatilities of real estate prices, few of those papers have noted one of the most important advantages of the housing market, its defensiveness, from the viewpoint of volatile behavior.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.803
Threshold uncertainty score0.209

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.030
GPT teacher head0.207
Teacher spread0.178 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it