Spatial GARCH Models for Unknown Spatial Locations - An Application to Financial Stock Returns

20 Pages Posted: 17 Oct 2022

See all articles by Markus J. Fülle

Markus J. Fülle

University of Goettingen (Göttingen)

Philipp Otto

Leibniz University Hannover

Date Written: October 5, 2022

Abstract

Finding a suitable weight matrix in spatial GARCH models is a challenge when the actual locations are not known. Thus, we introduce an estimation procedure for spatial GARCH models when the locations are unknown. We suggest to use balance sheet data of companies as proxy for the spatial distance between companies. We provide a simulation study underpinning the finite sample estimation performance of the method. As an empirical illustration, we use one year stock returns between 2019 and 2020 of the companies listed in the Dow Jones Global Titans 50 index. We find, that research and development expenses are most relevant for determining similar firms with regard to stock return volatility.

Keywords: Spatial GARCH, spatial weight matrix, stock returns, balance sheet, funancial risk spillover, unknown locations

Suggested Citation

Fülle, Markus J. and Otto, Philipp, Spatial GARCH Models for Unknown Spatial Locations - An Application to Financial Stock Returns (October 5, 2022). Available at SSRN: https://ssrn.com/abstract=4238805 or http://dx.doi.org/10.2139/ssrn.4238805

Markus J. Fülle (Contact Author)

University of Goettingen (Göttingen) ( email )

Platz der Gottinger Sieben 3
Gottingen, D-37073
Germany

Philipp Otto

Leibniz University Hannover

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
25
Abstract Views
157
PlumX Metrics