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Modelling Returns in US Housing Prices—You’re the One for Me, Fat Tails
Division of Economics, School of Business, Örebro University, Örebro, Sweden.
2 Division of Statistics, School of Business, Örebro University, Örebro, Sweden.ORCID iD: 0000-0002-0682-8584
Division of Economics, School of Business, Örebro University, Örebro, Sweden; National Institute of Economic Research, Stockholm, Sweden.ORCID iD: 0000-0002-4840-7649
2021 (English)In: Journal of Risk and Financial Management, E-ISSN 1911-8074, Vol. 14, no 11, p. 506-506Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
MDPI, 2021. Vol. 14, no 11, p. 506-506
Keywords [en]
non-Gaussianity; GARCH; probability integral transform; Kullback–Leibler information criterion
National Category
Economics
Identifiers
URN: urn:nbn:se:liu:diva-198442DOI: 10.3390/jrfm14110506ISI: 000725239800001Scopus ID: 2-s2.0-85165768712OAI: oai:DiVA.org:liu-198442DiVA, id: diva2:1804460
Available from: 2023-10-12 Created: 2023-10-12 Last updated: 2024-03-14Bibliographically approved

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Nguyen, Hoang

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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  • vancouver
  • oxford
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Language
  • de-DE
  • en-GB
  • en-US
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  • nn-NB
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Output format
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