Dynamic spatiotemporal ARCH models

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Otto P., Doğan O., Taşpınar S.

Spatial Economic Analysis, 2023 (SSCI) identifier identifier

  • Publication Type: Article / Article
  • Publication Date: 2023
  • Doi Number: 10.1080/17421772.2023.2254817
  • Journal Name: Spatial Economic Analysis
  • Journal Indexes: Social Sciences Citation Index (SSCI), Scopus, International Bibliography of Social Sciences, ABI/INFORM, Business Source Elite, Business Source Premier, EconLit, INSPEC
  • Keywords: GMM, house price returns, local real-estate market, Spatial ARCH, volatility, volatility clustering
  • Istanbul Technical University Affiliated: Yes


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. Preprint: This paper is based on the preprint arXiv:2202.13856.