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:

• Spatial GARCH
• spatial weight matrix
• stock returns
• balance sheet
• financial risk spillover
• unknown locations

JEL:

• C21
• C51
• C52
• F37

ACKNOWLEDGEMENT

We gratefully acknowledge financial support by the Deutsche Forschungsgemeinschaft (HE 2188/14-1) (412992257).

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the author(s).

Notes

1 For a discussion of identifiability, we refer the reader to Sato and Matsuda (2021).