Dynamic spatiotemporal ARCH models

Philipp Otto*, Osman Doğan, Süleyman Taşpınar

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


Geo-referenced data are characterised by an inherent spatial dependence due to geographical proximity. In this paper, we introduce a dynamic spatiotemporal autoregressive conditional heteroscedasticity (ARCH) process to describe the effects of (i) the log-squared time-lagged outcome variable, the temporal effect, (ii) the spatial lag of the log-squared outcome variable, the spatial effect, and (iii) the spatiotemporal effect on the volatility of an outcome variable. We derive a generalised method of moments (GMM) estimator based on the linear and quadratic moment conditions. We show the consistency and asymptotic normality of the GMM estimator. After studying the finite-sample performance in simulations, the model is demonstrated by analysing monthly log-returns of condominium prices in Berlin from 1995 to 2015, for which we found significant volatility spillovers.

Original languageEnglish
Pages (from-to)250-271
Number of pages22
JournalSpatial Economic Analysis
Issue number2
Publication statusPublished - 2024

Bibliographical note

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© 2023 Regional Studies Association.


  • GMM
  • Spatial ARCH
  • house price returns
  • local real-estate market
  • volatility
  • volatility clustering


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