Modelling returns in US housing prices: You're the one for me, fat tails

In this paper, we analysed the heavy-tailed behaviour in the dynamics of housing-price returns in the United States. We investigated the sources of heavy tails by estimating autoregressive models in which innovations can be subject to GARCH effects and/or non-Gaussianity. Using monthly data from Jan...

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Bibliographic Details
Published inJournal of risk and financial management Vol. 14; no. 11; pp. 1 - 17
Main Authors Kiss, Tamás, Nguyen, Hoang, Österholm, Pär
Format Journal Article
LanguageEnglish
Published Basel MDPI 2021
MDPI AG
Subjects
Collateral
Consumption
Economic crisis
Equilibrium
Error correction & detection
GARCH
Households
Housing
Housing prices
International finance
Kullback-Leibler information criterion
Kurtosis
Macroeconomics
non-Gaussianity
Normal distribution
probability integral transform
Propensity to consume
Skewness
Stochastic models
Variables
Volatility
United Kingdom > UK
United States > US
GARCH
Kullback–Leibler information criterion
non-Gaussianity
probability integral transform
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Summary:In this paper, we analysed the heavy-tailed behaviour in the dynamics of housing-price returns in the United States. We investigated the sources of heavy tails by estimating autoregressive models in which innovations can be subject to GARCH effects and/or non-Gaussianity. Using monthly data from January 1954 to September 2019, the properties of the models were assessed both within- and out-of-sample. We found strong evidence in favour of modelling both GARCH effects and non-Gaussianity. Accounting for these properties improves within-sample performance as well as point and density forecasts.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1911-8074
1911-8066
1911-8074
DOI:10.3390/jrfm14110506