Details
| Original language | English |
|---|---|
| Pages (from-to) | 92-105 |
| Number of pages | 14 |
| Journal | Spatial economic analysis |
| Volume | 19 |
| Issue number | 1 |
| Early online date | 6 Sept 2023 |
| Publication status | Published - 2024 |
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
ASJC Scopus subject areas
- Social Sciences(all)
- Geography, Planning and Development
- Economics, Econometrics and Finance(all)
- Decision Sciences(all)
- Statistics, Probability and Uncertainty
- Earth and Planetary Sciences(all)
- Earth and Planetary Sciences (miscellaneous)
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In: Spatial economic analysis, Vol. 19, No. 1, 2024, p. 92-105.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Spatial GARCH models for unknown spatial locations
T2 - an application to financial stock returns
AU - Fülle, Markus J.
AU - Otto, Philipp
N1 - Funding Information: We gratefully acknowledge financial support by the Deutsche Forschungsgemeinschaft (HE 2188/14-1) (412992257).
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - balance sheet
KW - financial risk spillover
KW - Spatial GARCH
KW - spatial weight matrix
KW - stock returns
KW - unknown locations
UR - http://www.scopus.com/inward/record.url?scp=85169909871&partnerID=8YFLogxK
U2 - 10.6084/m9.figshare.24092144.v1
DO - 10.6084/m9.figshare.24092144.v1
M3 - Article
AN - SCOPUS:85169909871
VL - 19
SP - 92
EP - 105
JO - Spatial economic analysis
JF - Spatial economic analysis
SN - 1742-1772
IS - 1
ER -