Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/258610 
Erscheinungsjahr: 
2021
Quellenangabe: 
[Journal:] Journal of Risk and Financial Management [ISSN:] 1911-8074 [Volume:] 14 [Issue:] 11 [Article No.:] 506 [Publisher:] MDPI [Place:] Basel [Year:] 2021 [Pages:] 1-17
Verlag: 
MDPI, Basel
Zusammenfassung: 
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.
Schlagwörter: 
non-Gaussianity
GARCH
probability integral transform
Kullback-Leibler information criterion
JEL: 
C22
C52
E44
E47
G17
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