Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/244571 
Year of Publication: 
2020
Series/Report no.: 
Working Paper No. 13/2020
Publisher: 
Örebro University School of Business, Örebro
Abstract: 
In this paper, we analyse the heavy-tailed behaviour in the dynamics of housing-price returns in the United States. We investigate 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 ranging from January 1954 to September 2019, the properties of the models are assessed both within- and out-of-sample. We find 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.
Subjects: 
Non-Gaussianity
GARCH
Density forecasts
Probability integral transform
JEL: 
C22
C52
E44
E47
G17
Document Type: 
Working Paper

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