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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Autores principales:	Tamás Kiss, Hoang Nguyen, Pär Österholm
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	non-Gaussianity GARCH probability integral transform Kullback–Leibler information criterion Risk in industry. Risk management HD61 Finance HG1-9999
Acceso en línea:	https://doaj.org/article/22de06518bbc4d3490a8e628e66142fe
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id	oai:doaj.org-article:22de06518bbc4d3490a8e628e66142fe
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spelling	oai:doaj.org-article:22de06518bbc4d3490a8e628e66142fe2021-11-25T18:08:22ZModelling Returns in US Housing Prices—You’re the One for Me, Fat Tails10.3390/jrfm141105061911-80741911-8066https://doaj.org/article/22de06518bbc4d3490a8e628e66142fe2021-10-01T00:00:00Zhttps://www.mdpi.com/1911-8074/14/11/506https://doaj.org/toc/1911-8066https://doaj.org/toc/1911-8074In 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.Tamás KissHoang NguyenPär ÖsterholmMDPI AGarticlenon-GaussianityGARCHprobability integral transformKullback–Leibler information criterionRisk in industry. Risk managementHD61FinanceHG1-9999ENJournal of Risk and Financial Management, Vol 14, Iss 506, p 506 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	non-Gaussianity GARCH probability integral transform Kullback–Leibler information criterion Risk in industry. Risk management HD61 Finance HG1-9999
spellingShingle	non-Gaussianity GARCH probability integral transform Kullback–Leibler information criterion Risk in industry. Risk management HD61 Finance HG1-9999 Tamás Kiss Hoang Nguyen Pär Österholm Modelling Returns in US Housing Prices—You’re the One for Me, Fat Tails
description	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.
format	article
author	Tamás Kiss Hoang Nguyen Pär Österholm
author_facet	Tamás Kiss Hoang Nguyen Pär Österholm
author_sort	Tamás Kiss
title	Modelling Returns in US Housing Prices—You’re the One for Me, Fat Tails
title_short	Modelling Returns in US Housing Prices—You’re the One for Me, Fat Tails
title_full	Modelling Returns in US Housing Prices—You’re the One for Me, Fat Tails
title_fullStr	Modelling Returns in US Housing Prices—You’re the One for Me, Fat Tails
title_full_unstemmed	Modelling Returns in US Housing Prices—You’re the One for Me, Fat Tails
title_sort	modelling returns in us housing prices—you’re the one for me, fat tails
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/22de06518bbc4d3490a8e628e66142fe
work_keys_str_mv	AT tamaskiss modellingreturnsinushousingpricesyouretheoneformefattails AT hoangnguyen modellingreturnsinushousingpricesyouretheoneformefattails AT parosterholm modellingreturnsinushousingpricesyouretheoneformefattails
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Modelling Returns in US Housing Prices—You’re the One for Me, Fat Tails

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