Spatial GARCH models for unknown spatial locations

an application to financial stock returns

verfasst von: Markus J. Fülle, Philipp Otto
Abstract: Spatial GARCH models, like all other spatial econometric models, require the definition of a suitable weight matrix. This matrix implies a certain structure for spatial interactions. GARCH-type models are often applied to financial data because the conditional variance, which can be translated as financial risks, is easy to interpret. However, when it comes to instantaneous/spatial interactions, the proximity between observations has to be determined. Thus, we introduce an estimation procedure for spatial GARCH models under unknown locations employing the proximity in a covariate space. We use one-year stock returns of companies listed in the Dow Jones Global Titans 50 index as an empirical illustration. Financial stability is most relevant for determining similar firms concerning stock return volatility.
Organisationseinheit(en): Institut für Kartographie und Geoinformatik
Externe Organisation(en): Georg-August-Universität Göttingen
Typ: Artikel
Journal: Spatial economic analysis
Band: 19
Seiten: 92-105
Anzahl der Seiten: 14
ISSN: 1742-1772
Publikationsdatum: 2024
Publikationsstatus: Veröffentlicht
Peer-reviewed: Ja
ASJC Scopus Sachgebiete: Geografie, Planung und Entwicklung, Volkswirtschaftslehre, Ökonometrie und Finanzen (insg.), Statistik, Wahrscheinlichkeit und Ungewissheit, Erdkunde und Planetologie (sonstige)
Elektronische Version(en): https://doi.org/10.6084/m9.figshare.24092144.v1 (Zugang: Offen)
https://doi.org/10.1080/17421772.2023.2237067 (Zugang: Geschlossen)

BibTeX

@article{2f49840348014a14a577d3456940e573,
title = "Spatial GARCH models for unknown spatial locations: an application to financial stock returns",
abstract = "Spatial GARCH models, like all other spatial econometric models, require the definition of a suitable weight matrix. This matrix implies a certain structure for spatial interactions. GARCH-type models are often applied to financial data because the conditional variance, which can be translated as financial risks, is easy to interpret. However, when it comes to instantaneous/spatial interactions, the proximity between observations has to be determined. Thus, we introduce an estimation procedure for spatial GARCH models under unknown locations employing the proximity in a covariate space. We use one-year stock returns of companies listed in the Dow Jones Global Titans 50 index as an empirical illustration. Financial stability is most relevant for determining similar firms concerning stock return volatility.",
keywords = "balance sheet, financial risk spillover, Spatial GARCH, spatial weight matrix, stock returns, unknown locations",
author = "F{\"u}lle, {Markus J.} and Philipp Otto",
note = "Funding Information: We gratefully acknowledge financial support by the Deutsche Forschungsgemeinschaft (HE 2188/14-1) (412992257). ",
year = "2024",
doi = "10.6084/m9.figshare.24092144.v1",
language = "English",
volume = "19",
pages = "92--105",
journal = "Spatial economic analysis",
issn = "1742-1772",
publisher = "Routledge",
number = "1",
}