Spatial GARCH models for unknown spatial locations

an application to financial stock returns

authored by: 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.
Organisation(s): Institute of Cartography and Geoinformatics
External Organisation(s): University of Göttingen
Type: Article
Journal: Spatial economic analysis
Volume: 19
Pages: 92-105
No. of pages: 14
ISSN: 1742-1772
Publication date: 2024
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: Geography, Planning and Development, Economics, Econometrics and Finance(all), Statistics, Probability and Uncertainty, Earth and Planetary Sciences (miscellaneous)
Electronic version(s): https://doi.org/10.6084/m9.figshare.24092144.v1 (Access: Open)
https://doi.org/10.1080/17421772.2023.2237067 (Access: Closed)

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",
}